HAROKOPIO UNIVERSITY

SCHOOL: DIGITAL TECHNOLOGY

DEPARTMENT: INFORMATICS & TELEMATICS

POSTGRADUATE PROGRAMME: MSc IN INFORMATICS & TELEMATICS

COURSE: INFORMATION SYSTEMS IN BUSINESS ADMINISTRATION

Main Title: Case Management Model and Notation «A Comparative study of CMMN tools for Knowledge process support: The

case of Smart Farming»

Master Thesis

Student’s name: Sotirios Koukoumtzis I.D. Number: 18604

Athens, 2021

HAROKOPIO UNIVERSITY

SCHOOL: DIGITAL TECHNOLOGY

DEPARTMENT: INFORMATICS & TELEMATICS

POSTGRADUATE PROGRAMME: MSc IN INFORMATICS & TELEMATICS

COURSE: INFORMATION SYSTEMS IN BUSINESS ADMINISTRATION

Examining Committee

Supervisor: Nikolaidou, Mara

Professor, Information & Telematics, Harokopio University

Examiner: Bardaki, Cleopatra

Assistant Professor, Information & Telematics, Harokopio University

Examiner: Tsadimas, Anargyros

Teaching Laboratory Staff, Information & Telematics, Harokopio University

Ethics and Copyright Statement (Required)

I, Sotirios Koukoumtzis, hereby declare that:

1) I am the owner of the intellectual rights of this original work and to the best of my knowledge, my work does not insult persons, nor does it offend the intellectual rights of third parties.

2) I accept that Library and Information Centre of Harokopio University may, without changing the content of my work, make it available in electronic form through its Digital Library, copy it in any medium and / or any format and hold more than one copy for maintenance and safety purposes.

TABLE OF CONTENTS

Abstract in Greek ............................................................................................................................................... 1

Keywords in Greek ......................................................................................................................................... 3

Abstract in English .............................................................................................................................................. 4

Keywords in English ........................................................................................................................................ 4

Table List ............................................................................................................................................................ 5

Picture List .......................................................................................................................................................... 5

1. Introduction ................................................................................................................................................ 7

2. Related Work ............................................................................................................................................ 10

2.1 Case Handling ......................................................................................................................................... 10

2.2 Business Model and Notation (BPMN)................................................................................................... 12

2.3 Decision Model and Notation (DMN) ..................................................................................................... 13

2.4 Business Models Mapping...................................................................................................................... 14

3. Knowledge-intensive Processes (KiPs) ..................................................................................................... 16

3.1 Knowledge-intensive Processes (KiPs) Definition .................................................................................. 16

3.2 Collaborative Knowledge Work (CKW) .................................................................................................. 17

3.3 Spectrum of Process Management ........................................................................................................ 18

3.4 Main Characteristics of Knowledge-Intensive Process (KiPs) ................................................................ 19

3.5 General Requirements for KiPs .............................................................................................................. 21

3.6 Case Management: Evaluation for Knowledge-Intensive Processes ..................................................... 26

4. Case Management Model and Notation (CMMN) ................................................................................... 27

4.1 CMMN Standard ..................................................................................................................................... 27

4.2 CMMN Elements, Notation and Description.......................................................................................... 28

4.3 Farm Management Information Systems .............................................................................................. 30

4.4 CMMN Empowers Smart Farming Processes ......................................................................................... 35

5. Smart Farming Case Study ........................................................................................................................ 35

5.1 Case Study Description ........................................................................................................................... 35

5.2 Smart Farming Case Study Model .......................................................................................................... 36

5.3 Smart Farming Model Description ......................................................................................................... 37

5.4 Characteristics Meet Requirements ....................................................................................................... 41

6. CMMN Tools ............................................................................................................................................. 44

6.1 Flowable .............................................................................................................................................. 44

6.2 Visual Paradigm .................................................................................................................................. 52

6.3 Trisotech ............................................................................................................................................. 55

6.4 Signavio ............................................................................................................................................... 58

6.5 Camunda ............................................................................................................................................. 60

6.6 BPMN.io .............................................................................................................................................. 62

6.7 CMMN Tools Overview ....................................................................................................................... 63

7. CMMN tool evaluation for KiPs ................................................................................................................ 64

7.1 Flowable Presentation ........................................................................................................................... 64

7.2 Visual Paradigm Presentation ................................................................................................................ 71

7.3 Trisotech Presentation ........................................................................................................................... 74

7.4 CMMN Tools Comparative Overview ..................................................................................................... 80

7.5 CMNN Tools Evaluation against KiPs Requirements .............................................................................. 83

8. Conclusion ................................................................................................................................................ 88

References ....................................................................................................................................................... 90

Page: 1

Abstract in Greek Στο πλαίσιο αυτής της διπλωματικής εργασίας, ασχοληθήκαμε με την διεξαγωγή μίας συγκριτικής μελέτης

εργαλείων CMMN για υποστήριξη διαδικασίας γνώσης, μελέτη περίπτωσης της έξυπνης καλλιέργειας.

Επιλέξαμε τον κλάδο των έξυπνων καλλιεργειών επειδή αποτελεί έναν νέο καινοτόμο κλάδο που

εξελίσσεται με ραγδαίους ρυθμούς ενσωματώνοντας στον πυρήνα του τις πιο πρόσφατες τεχνολογικές

καινοτομίες. Όπως την χρήση έξυπνων αισθητήρων και ένα δίκτυο από έξυπνες συσκευές “Internet Of

Things (IoT)”που συνεργάζονται μεταξύ τους και μπορούν να παράγουν πληροφορίες που οδηγούν στην

καλύτερη λήψη αποφάσεων και δημιουργούν μια βάση γνώσης που βοηθάει τον κλάδο να εξελίσσει

συνεχώς τις μεθόδους του και τις διαδικασίες των έξυπνων καλλιεργειών προσθέτοντας αξία και

εξελίσσουν τον κλάδο των έξυπνων καλλιεργειών και τον καθιστούν ελκυστικό. Οι διαδικασίες ενός

Συστήματος Πληροφοριών Διαχείρισης Φάρμας (FMIS), οδηγούνται κυρίως από γεγονότα, δεδομένα και

είναι άμεσα εξαρτημένες από τον ανθρώπινο παράγοντα. Απαιτούν έμπειρους και εξιδεικευμένους

εργαζόμενους οι οποίοι επηρεάζουν σημαντικά και καθορίζουν την εξέλιξη της πορείας των διαδικασιών

αυτών, που τις αποκαλούμε διαδικασίες εμπεριστατωμένης γνώσης, καθώς επίσης και την λειψή

αποφάσεων.

Εξετάζουμε την σημασία της έννοιας Διαδικασιών Εμπεριστατωμένης Γνώσης, απόρροιας των οποίον

δημιουργείται πλέον η ανάγκη για μια νέα γλώσσα μοντελοποίησης και σημειογραφίας, η οποία θα

ενσωματώνει στον πυρήνα της όλα αυτά τα χαρακτηριστικά, εργαλεία και τις μεθόδους για να καλύψει τις

ανάγκες μοντελοποίησης των παρόντων και μελλοντικών διαδικασιών που πλέον τείνουν να μην είναι

σαφώς ορισμένες, είναι ως επί το πλείστων αδόμητες, μοναδικές, η φύση τους οδηγείται από γεγονότα μη

προβλέψιμα και από επείγοντα γεγονότα. Οι διεργασίες εμπεριστατωμένης γνώσης, είναι οι διαδικασίες

των οποίων η συμπεριφορά και η εκτέλεση εξαρτώνται σε μεγάλο βαθμό από τους εργαζόμενους της

γνώσης που εκτελούν διάφορες αλληλοσυνδεόμενες εργασίες εντατικής λήψης αποφάσεων. Οι

Διαδικασίες Εμπεριστατωμένης Γνώσης είναι πραγματικές γνώσεις, πληροφορίες και κεντρικά δεδομένα

και απαιτούν ουσιαστική ευελιξία στο σχεδιασμό και το χρόνο εκτέλεσης. Εξετάζουμε τα κύρια

χαρακτηριστικά και τις απαιτήσεις Διαδικασιών Εμπεριστατωμένης Γνώσης.

O Χειρισμός Yπoθέσεων χρησιμοποιεί την υπόθεση ως την κεντρική ιδέα της διαδικασίας. Οι

δραστηριότητες είναι λιγότερο άκαμπτες από τις δραστηριότητες ροής εργασίας και αναμένεται ισορροπία

μεταξύ κεντρικών δεδομένων και κεντρικών διαδικασιών. Η διαδικασία δεν καθοδηγείται μόνο από τη ροή

της διαδικασίας, αλλά τα δεδομένα βοηθούν στην ροή της διαδικασίας. Οι εργαζόμενοι έχουν περισσότερο

έλεγχο, αλλά πρέπει να γνωρίζουν ολόκληρη την υπόθεση. Η ικανότητα εκτέλεσης, επανάληψης και

παράλειψης δραστηριοτήτων είναι σημαντική για την παροχή της απαιτούμενης ευελιξίας.

Η διαχείριση υποθέσεων είναι ένας τύπος τεχνολογίας επιχειρηματικής διαδικασίας που δεν χρησιμοποιεί

ροή ελέγχου για να περιγράψει τη διαδικασία. Η υπόθεση (αρχείο υπόθεσης ή φάκελος υπόθεσης) είναι η

κύρια ιδέα και περιέχει όλα τα δεδομένα και τις πληροφορίες σχετικά με τη διαδικασία. Η διαχείριση

υποθέσεων αφορά την ενδυνάμωση των εργαζομένων, παρέχοντάς τους πρόσβαση σε όλες τις

πληροφορίες που αφορούν την υπόθεση και δίνοντάς τους τη διακριτική ευχέρεια και τον έλεγχο του

τρόπου με τον οποίο εξελίσσεται μια υπόθεση. Η διαχείριση υποθέσεων δεν αφορά τη διαδικασία, αλλά

αφορά τους εργαζόμενους (Marin, 2016). Σε ένα παραδοσιακό σύστημα ροής εργασίας ή διαδικασίας ο

σχεδιαστής κωδικοποιεί τον επιχειρηματικό στόχο που πρέπει να επιτευχθεί στο μοντέλο. Στη συνέχεια, το

σύστημα είναι υπεύθυνο για τον επιχειρηματικό στόχο και χρησιμοποιεί τους εργαζομένους για την

επίτευξη αυτού του στόχου. Σε ένα σύστημα διαχείρισης υποθέσεων, από την άλλη πλευρά, οι εργαζόμενοι

είναι υπεύθυνοι για τον επιχειρηματικό στόχο και χρησιμοποιούν το σύστημα ως εργαλείο για την επίτευξη

Page: 2

αυτού του στόχου. Αυτός είναι ο λόγος για τον οποίο η διαχείριση υποθέσεων βασίζεται περισσότερο στην

κρίση του εργαζομένου παρά στη ροή ελέγχου.

Η Case Management Model and Notation (CMMN) είναι η γλώσσα που δημιουργήθηκε για να εξυπηρετήσει

αυτόν τον τύπο προσέγγισης. H CMMN ορίζει ένα κοινό μετα-μοντέλο και τη σημειογραφία για τη

μοντελοποίηση και την γραφική έκφραση μιας υπόθεσης καθώς και μια εναλλαγή μορφής για την

ανταλλαγή μοντέλων περιπτώσεων μεταξύ διαφορετικών εργαλείων. Η CMMN προορίζεται να συλλάβει τα

κοινά στοιχεία που χρησιμοποιούν τα προϊόντα διαχείρισης περιπτώσεων, λαμβάνοντας επίσης υπόψη τις

τρέχουσες ερευνητικές συνεισφορές στη διαχείριση υποθέσεων. Γνωστό ως Adaptive Case Management, η

CMMN βοηθά στη διαδικασία λήψης αποφάσεων μέσω προτάσεων, αλλά διατηρεί τους ανθρώπους

σταθερά στη θέση του οδηγού. Η CMMN επικεντρώνεται σε ζωντανές πληροφορίες και σχέσεις, ενώ οι

παραδοσιακές επιχειρηματικές διαδικασίες επικεντρώνονται σε προκαθορισμένες ακολουθίες μίας

διαδικασίας. Η CMMN είναι ένας γραφικός συμβολισμός που χρησιμοποιείται για τη σύλληψη μεθόδων

εργασίας, οι οποίες βασίζονται στον χειρισμό περιπτώσεων όπου απαιτούνται από διάφορες

δραστηριότητες που μπορούν να εκτελεστούν με απρόβλεπτη σειρά ως απόκριση σε εξελισσόμενες

καταστάσεις. Χρησιμοποιώντας μια προσέγγιση με επίκεντρο τα γεγονότα και την έννοια ενός αρχείου

περιπτώσεων, η CMMN επεκτείνει τα όρια του τι μπορεί να μοντελοποιηθεί με την Business Process

Management and Notation (BPMN), συμπεριλαμβανομένων των λιγότερο δομημένων διεργασιών και

αυτών που καθοδηγούνται από τους έμπειρους και εξειδικευμένους εργαζόμενους. Η χρήση ενός

συνδυασμού της BPMN και της CMMN επιτρέπει στους χρήστες να καλύπτουν ένα πολύ ευρύτερο φάσμα

μεθόδων εργασίας.

Το πρόβλημα μας είναι ότι η γλώσσα μοντελοποίησης και σημειογραφίας CMMN είναι μία πολύ νέα

γλώσσα στον τομέα της μοντελοποίησης της Object Modelling Group (OMG). Ωστόσο είδη υπάρχει μια

αξιόλογη ποικιλία από εργαλεία που να υποστηρίζουν την γλώσσα CMMN, όμως προς το παρόν δεν έχει

πραγματοποιηθεί κάποια μελέτη για αυτά τα εργαλεία, που να αναδεικνύει τον βαθμό που αυτά τα

εργαλεία CMMN υποστηρίζουν τις σύγχρονες διαδικασίες εμπεριστατωμένης γνώσης, όπου αποτελούν το

νέο περιβάλλον διαδικασιών που συναντούμε στην διαχείριση ενός σύγχρονου επιχειρησιακού

περιβάλλοντος.

Σκοπός αυτής της διπλωματικής εργασίας είναι να κάνει μια συγκριτική μελέτη των πιο αντιπροσωπευτικών

εργαλείων CMMN με γνώμονα την βαθμό που υποστηρίζουν τις διαδικασίες εμπεριστατωμένης γνώσης.

Για να το πετύχουμε αυτό θα εξετάσουμε μία μελέτη περίπτωσης έξυπνων καλλιεργειών που διαθέτει τα

χαρακτηριστικά και τις απαιτήσεις μίας ομάδας διαδικασιών εμπεριστατωμένης γνώσης. Θα

παρουσιάσουμε τα πιο αντιπροσωπευτικά εργαλεία της γλώσσας CMMN που ήδη υπάρχουν και στην

συνέχεια θα επιλέξουμε τρία από αυτά, θα τα αναλύσουμε και θα σχεδιάσουμε το μοντέλο μας που

προκύπτει από την μελέτη περίπτωσης έξυπνων καλλιεργειών. Πιο συγκεκριμένα τις διαδικασίες ενός

σύγχρονου θερμοκηπίου. Την διαχείριση των πληροφοριών που προέρχονται από τους έξυπνους

αισθητήρες που διαθέτει για τις απαραίτητες μετρήσεις του και την καταγραφή των συνθηκών του, όπως

η θερμοκρασία του αέρας, έδαφος, μετρήσεις, υγρασία στο περιβάλλον του θερμοκηπίου, την

παρακολούθηση των καιρικών φαινομένων καθώς και την συμβολή και συμμετοχή έμπειρων και

εξειδικευμένων εργαζομένων στη ροή και τον έλεγχο αυτών των διαδικασιών καθώς και στη λήψη

αποφάσεων.

Στην συνέχεια θα αξιολογήσουμε τα τρία αυτά εργαλεία βασιζόμενοι κυρίως στις μεθόδους χαρακτηρισμού

των KiPs που προκύπτουν από τον Ciccio, και τις μεθόδους προσέγγισης διαδικασιών εμπεριστατωμένης

γνώσης που προκύπτουν από τον Marin. Βάση αυτών θα αξιολογήσουμε τα εργαλεία CMMN κατά τη

διάρκεια της φάσης μοντελοποίησης και εκτέλεσης εξετάζοντας τον βαθμό που καλύπτουν τις απαιτήσεις

αυτών των σύγχρονων διαδικασιών εμπεριστατωμένης γνώσης.

Page: 3

Παρουσιάζουμε τα κύρια χαρακτηριστικά της γλώσσα μοντελοποίησης «Μοντέλο και Σημειογραφία

Διαχείρισης Υποθέσεων» (CMMN). Εξετάζουμε πώς εξελίχθηκαν τα Πληροφοριακά Συστήματα Διαχείρισης

Φάρμας στις μέρες μας με την τεράστια εξέλιξη της τεχνολογίας και τον τρόπο με τον οποίο η CMMN

ενδυναμώνει τις έξυπνες διαδικασίες καλλιέργειας που ενσωματώνουν μέσα τους την ανάγκη για

έμπειρους εργαζόμενους που επηρεάζουν και καθορίζουν με την γνώση τους την εξέλιξη και την ροή των

διαδικασιών και την νέα φύση αυτών των διαδικασιών που οδηγούνται από τα δεδομένα και τα συμβάντα

που λαμβάνουν χώρα στο περιβάλλον τους.

Εξετάζουμε μία μελέτη περίπτωσης μέσα από των χώρο των έξυπνων καλλιεργειών, που έχει να κάνει με

τις διαδικασίες ενός θερμοκηπίου, την διαχείριση από τα συμβάντα πληροφοριών που έρχονται σαν είσοδο

από τους έξυπνους αισθητήρες που διαθέτει ένα σύγχρονο θερμοκήπιο για την μετρήσεις των συνθηκών

του όπως η θερμοκρασία του αέρα, του εδάφους, η μετρήσεις τις υγρασίας του εδάφους, τις υγρασίας που

υπάρχει στο περιβάλλον του θερμοκηπίου και την παρακολούθησή των καιρικών φαινομένων καθώς και

την συμβολή - εμπλοκή των έμπειρων και εξειδικευμένων εργαζομένων στην ροή, των έλεγχο αυτών των

διαδικασιών καθώς επίσης στην λήψη αποφάσεων.

Παραθέτουμε αναλυτικά τα πιο γνωστά CMMN εργαλεία που κυριαρχούν στον χώρο της μοντελοποίησης

της γλώσσας αυτής, που είναι το Flowable, Visual Paradigm, Trisotech, Signavio, Camunda και BPMN.io.

Αναφέρουμε τα κύρια χαρακτηριστικά τους τα δυνατά και τις αδυναμίες τους που ίσως υπάρχουν σε κάποια

από αυτά.

Σχεδιάζουμε το μοντέλο που προκύπτει από την μελέτη περίπτωσης με τα πιο δημοφιλή προϊόντα

μοντελοποίησης της γλώσσας CMMN, που κατά την γνώμη μας είναι το Flowable, Visual Paradigm και το

Trisotech. Μελετούμε την πολυπλοκότητα τους και της εμπειρίας της οποίας απαιτούν στην μοντελοποίηση

των υποθέσεων. Αναδεικνύουμε τις διαφορές τους και αξιολογούμε σε πιο βαθμό καλύπτουν τις κύριες

απαιτήσεις των διαδικασιών εμπεριστατωμένης γνώσης τόσο στην φάση της σχεδίαση όσο και κατά την

εκτέλεση τους.

Keywords in Greek Διαχείριση Φάρμας, Πληροφοριακά Συστήματα, Γεωργία ακριβείας, Μοντέλο Διαχείρισης Υποθέσεων και

Σημειογραφία, Εμπεριστατωμένη Γνώση, Διαδικασίες Εμπεριστατωμένης Γνώσης, Διαχείριση Υπόθεσης,

Συνεργατική Εργασία Γνώσης, Συστήματα Διαχείρισης Διαδικασίας.

Page: 4

Abstract in English In this dissertation, we conducted a comparative study of CMMN tools for knowledge process support, the

case of Smart Farming. We chose the Smart Farming industry because it is an innovative industry that is

constantly incorporating the latest technologies and is constantly evolving into a technology industry that is

changing processes and practices to keep pace with the new digital world of internet of things, smart device,

interconnection and collaboration.

Business modeling can be seen as a practice to facilitate change in the business by identifying proposed

Information and Communication Technology (ICT) solutions that provide value to businesses. Business

modeling and business analytics can be linked to different business models, techniques and software tools.

Case Management uses the case as the central idea of the process. The activities are less rigid than the

workflow activities and a balance between central data and central processes is expected. The process is not

only guided by the process flow, but the data helps in the process flow. Employees have more control, but

they need to know the whole thing. The ability to perform, repeat and skip activities is important to provide

the required flexibility.

We examine the importance of the concept of Knowledge-Intensive Processes. Knowledge-Intensive

processes are the processes whose behavior and execution depend to a large extent on knowledge workers

performing various interrelated intensive decision-making tasks. Knowledge-Intensive Processes are real

knowledge, information and central data and require substantial flexibility in design and execution time. We

examine the main features and requirements of Knowledge-Intensive Processes.

We present the main features of the modeling language "Case Management Model and Notation" (CMMN).

We look at how Farm Management Information Systems have evolved today with the tremendous

advancement of technology and how CMMN empowers smart farming processes that incorporate the need

for experienced workers who knowingly influence and determine evolution and the flow of processes and

the new nature of these processes driven by the data and events that take place in their environment.

We examine a case study through the field of Smart Farming, which has to do with the processes of a

greenhouse, the management of information events that come as input from the smart sensors that a

modern greenhouse has to measure its conditions such as temperature of air, soil, measurements, humidity

in the greenhouse environment and monitoring of weather phenomena as well as the contribution -

involvement of experienced and specialized workers in the flow, control of these processes as well as on the

decision making.

We quote in detail the most well-known CMMN tools that dominate in the field of modeling this language,

which are Flowable, Visual Paradigm, Trisotech, Signavio, Camunda and BPMN.io. We mention their main

characteristics, their strengths and weaknesses that may exist in some of them.

We design the model resulting from the case study with the most popular CMMN language modeling

products, which in my opinion are Flowable, Visual Paradigm and Trisotech. We study their complexity and

the experience they require in modeling cases. We highlight their differences and evaluate what extent they

meet the main requirements of in-depth knowledge processes both in the design phase and during the

execution phase.

Keywords in English Farm Management, IOT, Information Systems, Precision Agriculture, Case Management Model and Notation,

CMMN, Knowledge-Intensive, Knowledge-Intensive Processes, KiPs, Case Management, Collaborative

Knowledge Work, CKW, Process Management Systems, PMS.

Page: 5

Table List Table - 1 Scope of BPMS, CMMN and DMN .................................................................................................... 14

Table - 2 KiPs Requirements and the Mainly Included Characteristics (Claudio Di Ciccio, Andrea Marrella,

Alessandro Russo, 2015) .................................................................................................................................. 25

Table - 3 CMMN Elements, Notation and Description. ................................................................................... 29

Table - 4 Farm Management Information Systems (Fountas S, Carli C, Sorensen CG, Tsiropoulos Z, Cavalaris

C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B, 2015a) ..................................................... 32

Table - 5 CMMN Elements that required to meet the requirements .............................................................. 43

Table - 6 Flowable CMMN Elements ................................................................................................................ 66

Table - 7 Visual Paradigm CMMN Elements. ................................................................................................... 72

Table - 8 Trisotech Case Modeler CMMN Elements. ....................................................................................... 76

Table - 9 Summarizing the top three CMMN tool’s Evaluation. ...................................................................... 82

Table - 10 CMMN Tool Evaluation ................................................................................................................... 83

Picture List Picture- 1 Business Models Mapping (M. Pankowska, 2019). ......................................................................... 15

Picture- 2 Collaborative knowledge work life cycle (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo,

2015) ................................................................................................................................................................ 17

Picture- 3 The spectrum of process management (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo,

2015) ................................................................................................................................................................ 18

Picture- 4 Fundamental components of a KiP (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015)

.......................................................................................................................................................................... 21

Picture- 5 Generic framework for the structuring of KiPs with case management, (Mike A. Marin, Matheus

Hauder, Florian Matthes, 2015). ...................................................................................................................... 26

Picture- 6 Farm Management Information System (Sorensen et al. 2010) .................................................... 32

Picture- 7 Farm Machinery Management Information System (Sorensen et al. 2010) .................................. 33

Picture- 8 Smart Farming, Smart Management System of the Greenhouse (BPMN.io CMMN Editor/Viewer).

.......................................................................................................................................................................... 36

Picture- 9 Irrigation System Stage Plan. ........................................................................................................... 37

Picture- 10 Watering Plants Stage Plan. .......................................................................................................... 38

Picture- 11 Rooftop Management. .................................................................................................................. 38

Picture- 12 Air Purification System. ................................................................................................................. 39

Picture- 13 Critical Milestones. ........................................................................................................................ 39

Picture- 14 Flowable Technology (Flowable, n.d.) ........................................................................................... 44

Picture- 15 Flowable Work (Flowable, n.d.) .................................................................................................... 45

Picture- 16 Flowable Orchestrate (Flowable, n.d.) .......................................................................................... 46

Picture- 17 Flowable Engage (Flowable, n.d.) .................................................................................................. 47

Picture- 18 Flowable Powerful Suit Technology (Flowable, n.d.) .................................................................... 48

Picture- 19 Flowable CMMN orchestrate BPMS and DMN (Flowable, n.d.) ................................................... 49

Picture- 20 (Visual Paradigm, n.d.) .................................................................................................................. 52

Picture- 21, (Trisotech Digital Modeling Suite, n.d.) ........................................................................................ 55

Picture- 22 Signavio Business Transformation Suit (Signavio, n.d.) ................................................................ 58

Picture- 23 Camunda Modeler Design Editor (DEEHAN, 2020) ....................................................................... 60

Picture- 24 Camunda Modeler (DEEHAN, 2020) .............................................................................................. 61

Picture- 25 BPMN.io Modeler Design Editor, (BPMN.IO, n.d.) ........................................................................ 62

Page: 6

Picture- 26 Flowable design environment. ...................................................................................................... 64

Picture- 27 Flowable Process Task Settings With a simple BPMS Process Example. ...................................... 67

Picture- 28 Flowable Form Reference of Human Task Emergent Critical Event Handling Expert. .................. 67

Picture- 29 Flowable Engage. ........................................................................................................................... 68

Picture- 30 Flowable design Smart Farming Case diagram. ............................................................................ 69

Picture- 31 Flowable App publishing. .............................................................................................................. 70

Picture- 32 Flowable App publishing. .............................................................................................................. 70

Picture- 33 Visual Paradigm CMMN Design Environment. .............................................................................. 71

Picture- 34 Visual Paradigm design Smart Farming Greenhous Case diagram. .............................................. 73

Picture- 35, Case Modeler (Trisotech Digital Modeling Suite, n.d.) ................................................................ 74

Picture- 36 Trisotech Case Modeler Design Environment. .............................................................................. 75

Picture- 37 Trisotech Case Modeler Import-Export. ........................................................................................ 76

Picture- 38 Trisotech Case Modeler Extra Task Setting. .................................................................................. 76

Picture- 39 Trisotech Case Modeler Design Smart Farming Greenhous Case diagram. ................................. 77

Picture- 40 Trisotech Teamwork. ..................................................................................................................... 78

Picture- 41 Trisotech Case Animator. .............................................................................................................. 78

Picture- 42 Trisotech Case Animator Example Presentation. .......................................................................... 79

Picture- 43 Trisotech Digital Automation Suit. ................................................................................................ 79

Picture- 44 Visual Paradigm Overview, (Visual Paradigm, n.d.). ..................................................................... 80

Picture- 45 Visual Paradigm Overview, (Flowable, n.d.).................................................................................. 80

Picture- 46, Digital Enterprise Suite (Trisotech Digital Modeling Suite, n.d.). ................................................. 81

Page: 7

1. Introduction

The scope of this dissertation is to make a Comparative study of CMMN tools for Knowledge process support:

The case of Smart Farming.

We chose the smart farming industry because it is a new innovative industry that is evolving rapidly,

incorporating the latest technological innovations at its core. Such as the use of smart sensors and a network

of smart devices "Internet of Things (IoT)" that work together and can exchange information that leads to

better decision making and create a knowledge base that helps the industry to constantly evolve its methods.

The processes of a Farm Management Information System (FMIS) are driven mainly by events, data and are

directly dependent on the human factor. They require experienced and skilled employees who significantly

influence and determine the progress of these processes, which we call thorough knowledge processes, as

well as the decision making.

In nowadays, modern Business Companies require in order to achieve their business goals to be composed

of experienced, specialized personnel who can perform many tasks independently. The traditional strictly

structured procedures that we know in advance the order and how to execute them from beginning to end

and could be modeled with the traditional modelling language and notation BPMS, are enriched by new

procedures that integrate data during the execution of procedures and require considerable flexibility at

runtime, are largely unstructured, and are highly dependent on the human factor to determine their

evolution and mode of execution. These processes are called Knowledge-intensive processes (KiPs).

Knowledge-intensive processes (KiPs) integrate data in the execution of processes and require substantial

amount of flexibility at run-time. Due to the lower level of predictability compared to routine processes, KiPs

need to balance between structured elements for repetitive aspects and unstructured elements to allow

creative solutions for complex problems. KiPs processes are goal oriented, emergent, and knowledge

creating. Process models for highly structured routine processes are not suitable for KiPs since they would

become too complex to manage and maintain. KiPs require more emphasis on collaboration and creativity

compared to highly structured processes.

It is important of understanding the main features and requirements of knowledge-intensive processes.

knowledge-intensive processes create the need for a new modelling and notation language, which will

incorporate at its core all these features, tools and methods to meet the needs of modelling current and

future processes that now tend to be not clearly defined, are mostly unstructured, unique, their nature is

driven by unpredictable events and urgent events.

Case management is a type of business process technology that does not use a flow of control to describe

the process. The case (case file or case file) is the main idea and contains all the data and information about

the process. Case management is about empowering employees, giving them access to all the information

about the case and giving them the discretion and control over how a case unfolds. Case management is not

about the process, but about the employees (Marin, 2016). In a traditional workflow or process system, the

designer codifies the business goal to be achieved in the model. The system is then responsible for the

business goal and uses the employees to achieve that goal. In a case management system, on the other hand,

employees are responsible for the business goal and use the system as a tool to achieve that goal. This is

why case management is based more on employee judgment than on the flow of control.

Case Management Model and Notation (CMMN) is the language created to serve this type of approach. The

CMMN defines a common meta-model and the notation for modelling and graphical expression of a

hypothesis as well as a format switch for exchanging case models between different tools. The CMMN is

Page: 8

intended to capture common elements used in case management products, also taking into account current

research contributions to case management. Known as Adaptive Case Management, CMMN helps with the

decision-making process through suggestions, but keeps people firmly in the driver's seat. CMMN focuses

on live information and relationships, while traditional business processes focus on predefined sequences of

a process.

CMMN is a graphical notation used to capture working methods, which are based on handling cases where

they are required by various activities that can be performed in an unpredictable order in response to

evolving situations. Using an event-focused approach and the concept of a case file, CMMN extends the

boundaries of what can be modelled with Business Process Management and Notation (BPMN), including

less structured processes and those guided by experienced and skilled professionals. Using a combination of

BPMN and CMMN allows users to cover a much wider range of working methods.

Our problem is that the CMMN modelling and notation language is a very new language in the Object

Modelling Group (OMG) modelling field. However, there is a considerable variety of tools that support the

CMMN language, but at present no study has been done on these tools, which highlights the extent to which

these CMMN tools support modern knowledge-intensive processes, where they are the new process

environment, we encounter in managing a modern business environment.

The purpose of this dissertation is to make a comparative study of the most representative CMMN tools

based on the degree to which they support knowledge-intensive processes. To achieve this we will look at a

case study of smart farming that meets the characteristics and requirements of a set of thorough knowledge-

intensive processes.

We will present the most representative tools of the CMMN language (Flowable, Visual Paradigm, Trisotech,

Signavio, Camunda and BPMN.io) that already exist and then we will select three of them (Flowable, Visual

Paradigm and Trisotech), analyse them and design our model resulting from the case study of smart farming.

More specifically the case study consists of the knowledge-intensive processes of a modern greenhouse. The

management of information coming from the smart sensors at its disposal for the necessary measurements

and the recording of its conditions, such as air temperature, soil, measurements, humidity in the greenhouse

environment, monitoring of weather conditions as well as the contribution and participation of experienced

and specialized employees in the flow and control of these processes as well as in decision making.

We will then evaluate these three tools (Flowable, Visual Paradigm and Trisotech) based mainly on methods

of characterizing KiPs derived from Ciccio, and methods of approaching knowledge-intensive processes

derived from Marin. Based on this, we will evaluate the CMMN tools during the modelling and execution

phase by examining the extent to which they meet the requirements of these modern knowledge-intensive

processes.

In the following chapters we examine the related work that exists in the management of business processes

(Case Handling, Business Model and Notation, Decision Model and Notation and Business Models Mapping).

We examine the knowledge-intensive process (KiPs Definition, Collaborative Knowledge Work, Spectrum of

Process Management, Main Characteristics of KiPs, General Requirements for KiPs, and Case Management

Evaluation for KiPs).

We examine the CMMN Modelling and Notation Language (CMMN Standard, CMMN Elements, Notation

and Description, Farm Management Information Systems and CMMN Empowers Smart Farming Process).

We are examining the case study of smart farming (Case Study description, Smart farming Case Study Model,

Smart Farming Model Description and Characteristics Meet Requirements).

Page: 9

We present the most representative tools of the CMMN language (Flowable, Visual Paradigm, Trisotech,

Signavio, Camunda, BPMN.io and CMMN Tools Overview).

We present the CMMN Tool evaluation for KiPs (Flowable Presentation, Visual Paradigm Presentation and

Trisotech Presentation CMMN Tools Evaluation and CMMN Tools Evaluation Overview) and finally we

present the final conclusions.

Page: 10

2. Related Work Business Process Management (BPM), is an active field of research, which is of great importance from a

practical point of view, while offering many technical challenges. It is based on the observation that every

product or service that a company provides to the market is the result of a series of activities performed.

Business processes are the key tools for organizing these activities and for improving the understanding of

their relationships. BPM aims to provide techniques and software for designing, establishing, controlling,

and analyzing business processes involving people, organizations, documents, and other sources of

information. The Process Management System (PMS) is a software system guided by explicit representations

of processes called process models and are responsible for coordinating the establishment of business

processes. Process models are the basic artifacts to support the process through a PMS, as they provide an

explicit representation of process knowledge.

Integrated approaches develop modelling activities in three main dimensions, the flow-control perspective,

which describes the structure of a process in terms of tasks and the relationships between them, the data

perspective, which describes data elements that are consumed, produced and exchanged during execution

of the process; and the perspective of resources, describing the operational and organizational framework

for carrying out the process in terms of resources to the extent that they are capable.

A PMS that takes in input a process model is able to manage the process routing by deciding which tasks are

enabled for execution and by assigning them to proper resources. A single execution of a process model

within the engine of the PMS is called process instance. PMS hold the promise of facilitating the everyday

operation of many enterprises and work environments, by supporting business processes in all the steps of

their life cycle. The life cycle of a business process is organized in four main stages. In the design phase,

starting from a requirements analysis, process models are designed using a suitable modelling language. In

the configuration phase, process models are implemented by configuring a PMS that supports process

enactment. In the enactment phase, process instances are then initiated, executed and monitored by the

run-time environment, and performed tasks generating execution traces are tracked and logged. Finally, in

the diagnosis phase, process logs are evaluated and mined to identify problems and possible improvements,

potentially resulting in process re-design and evolution.

In previous years, Business Process Management and Notation (BPMN) tools have been developed for

business processes, and today Business Process Management environments provide wide support for

different modelling styles and for all phases of the process life cycle. Process management approaches are

often based on the assumption that processes are characterized by repeated tasks, which are performed on

the basis of a process model prescribing the execution flow in its entireness. This kind of structured work

includes mainly production and administrative processes. However, the current maturity of process

management methodologies has led to the application of process-oriented approaches in new challenging

knowledge-intensive scenarios, such as Smart Farming.

The first thing that would be good to describe is what Case Handling is.

2.1 Case Handling Case Handling was first introduced by van der Aalst et al. (Aalst, W.M.P.V.D., Berens, P.J.S., 2001) in 2001. It uses the case as the central concept for the process. Activities are less rigid than workflow activities and a balance between data-centric and process-centric is expected. The process is not driven just by the process flow, but the data helps drive the process. Workers do have more control, but they need to be aware of the whole case. The ability to execute, redo, and skip activities are important to provide the required flexibility. More features of case handling were identified by van der Aalst, et al. (Aalst, W.M.P.V.D., Weske, M., Grunbauer, D.: Case Handling, 2005) in 2005:

Page: 11

1. Case handling avoids context tunneling (provide case workers with all the information about the case, instead of narrowing the information to the activity).

2. Case handling is data driven (enable activities based on the available information instead of only using control flow).

3. Case handling separates work distribution from authorization (query mechanisms can be used to navigate through active cases).

4. Case handling allows workers to view, add, and modify data outside an activity.

The characteristics of case handling systems were described by Reijers. (Reijers, H.A., Rigter, J., Aalst, W.M.P.V.D., 2003) As three, first the system's focus is on the case, second the process is data driven, and third parts of the process model are implicit. In a traditional workflow, the designer specifies what is permitted (explicit modelling). Modelling in case handling is less prescriptive where only the preferred or normal path is modeled (implicit modelling). Case handling treats both data and process as first-class citizens (Aalst, W.M.P.V.D., Weske, M., Grunbauer, D.: Case Handling, 2005). Case handling concepts were implemented in a set of products that included Flower of Pallas Athena, the Staff ware Case Handler, and the COSA Activity Manager (Aalst, W.M.P.V.D., Weske, M., Grunbauer, D.: Case Handling, 2005). Kaan et al. (Kaan, K., Reijers, H.a., Molen, P.V.D., 2006) introduce Case Management as an alternative to Case Handling. The flexibility required by case handling do impair some of the advantages of the workflow technology (Kaan, K., Reijers, H.a., Molen, P.V.D., 2006). The authors see case handling as an alternative to workflow (Reijers, H.A., Rigter, J., Aalst, W.M.P.V.D., 2003), (Kaan, K., Reijers, H.a., Molen, P.V.D., 2006). Case Management as defined by Kaan et al. (Kaan, K., Reijers, H.a., Molen, P.V.D., 2006) enhances workflow technology by focusing on the tasks. The control ow between tasks is retained, but a task is decomposed into work content and activities. The work content provides the flexibility required by case management without compromising the control ow provided by the workflow. This initial definition of Case Management is at odd with current-definitions, however it helps to clarify the distinction between case handling and case management. With the exception of Berkeley and Eccles (Berkley, J.D., Eccles, R.G., 1991) and Davenport and Nohria (Davenport, T., Nohria, N., 1994), until this moment in time definitions of case handling and case management were technology based and relied on particular tool implementations. Further evolution of the term case management happened at the end of the 2000 decade, when market analysts started defining the term. The definition changed from an implementation definition into a more general market definition. The term case management evolved into a method or practice that could be implemented in multiple ways by different products. Several market analysts including Heiser et al. (Hauder, M., Kazman, R., Matthes, F., 2015) in 2007 at Gartner Inc., Kerremans (Kerremans, M., 2008) also at Gartner Inc., and White (White, M., 2009) at Business Process Trends (BPTrends) popularized the term Case Management. They emphasized the collaboration nature of case management and the flexible interaction between humans, content, and processes. Kerremans defined case management work as collaborative and non-deterministic, where the work depends more on human decision making and content than in a predefined-processes (Kerremans, M., 2008). Clair et al. [6] in 2009 at Forrester Research introduced the term Dynamic Case Management. Clair et al. (Clair, L.C., Moore, C., Vitti, R., 2009) define dynamic case management as highly structured but collaborative, dynamic, information intensive processes driven by events. The case folder contains all the information needed to process and manage the case. The definition is consistent the other market analyst definitions like (Heiser, J., Lotto, R.J.D., 2007), (Kerremans, M., 2008), (White, M., 2009). Swenson (Swenson KD, 2010) popularized the term Adaptive Case Management. However, just in Swenson (Swenson KD, 2010) there are five distinct definitions of case management by different authors. Three definitions of case management including the glossary (de Man, H., Prasad, S., van Donge, T., 2010), (McCauley, D., 2010), (Swenson KD, 2010), and two different definitions of adaptive case management (Palmer, N., 2010), (Swenson KD, 2010).

Page: 12

Pucher (Pucher, M.J.) understand dynamic case management as being dynamic at runtime, versus adaptive case management in which the case is created just-in-time as needed. In addition, Pucher (Pucher, M.J.) view of adaptive case management implies case adaptation based on previous instances. Emerging case management defined by Bohringer (Bohringer, M., 2011) suggests a bottom-up view on case management that leverages social software techniques like micro-blogging, activity streams and tagging. Swenson (Swenson, K.D., 2013), Motahari-Nezhad and Swenson (Motahari-Nezhad, H.R., Swenson, K.D.) distinguish between Adaptive Case Management (ACM) and Production Case Management (PCM). Both of them are compliant with a generic definition of case management. The distinction is based on who creates the case template and when it is created. In ACM, the case template is created by the knowledge worker at the moment that it is needed. In PCM, the case template is created by developers during a design phase, and it is then used by the knowledge workers. Both ACM and PCM, allow knowledge workers high degree of flexibility and discretion on how to complete the case. As a conclusion summarizing all the above, we attempt the following definition: Case management is a practice for knowledge-intensive processes with a case folder as central repository, whereas the course of action for the fulfillment of goals is highly uncertain and the execution gradually emerges according to the available knowledge base and expertise of knowledge workers. At this point it is good to say a few words about Business Model and Notation (BPMN) and Decision Model and Notation (DMN) in order to understand better the role that playing in Business Process Management (BPM).

2.2 Business Model and Notation (BPMN) Business Model and Notation (BPMS), is a standard for business process modelling that provides graphical

notation for specifying business processes in a Business Process Diagram (BPD), based on traditional

flowcharting techniques. The objective of BPMN is to support business process modelling for both technical

users and business users, by providing notation that is intuitive to business users, yet able to represent

complex process semantics. The BPMN 2.0 specification also provides execution semantics as well as

mapping between the graphics of the notation and other execution languages. These include the business

analysts who create and refine the processes, the technical developers responsible for implementing them,

and the business managers who monitor and manage them. Consequently, BPMN serves as a common

language, bridging the communication gap that frequently occurs between business process design and

implementation (M von Rosing, 2015). The Business Process Model and Notation (BPMN) is the de-facto

standard for representing in a very expressive graphical way the processes occurring in virtually every kind

of organization one can think of, from cuisine recipes to the Nobel Prize assignment process, incident

management, e-mail voting systems and travel booking procedures etc. (M Chinosi, 2012). The focus of

BPMN is to enhance primary process modelling capabilities. It does not attempt to model other business

models, such as organization, strategic direction, business functions, rules/compliance aspects, etc.

Therefore, it is vital to understand that other types of modelling done by organizations outside the primary

process purposes are out of scope for BPMN, but they all fit within larger BPM solutions. Below is therefore

a specification of modelling principles and concepts excluded from BPMN:

✓ The linking of business strategies, critical success factors, and value drivers to processes.

✓ The relation between organizational structures, including business competencies, capabilities, and

resources to processes.

✓ Functional breakdowns of business functions into process tasks.

✓ Arrangement of business objects such as product, machine, warehouse, and so on, throughout the

process models.

✓ Specification of information objects and thereby information flow within the process models.

Page: 13

✓ The ability to illustrate or model business measurement that is, Key Performance Indicators or

Process Performance Indicators (PPIs) within the process.

✓ Data models, whereas BPMN shows the flow of data (messages), and the association of data artifacts

to activities, it is not a data model or even a data flow diagram.

✓ Even though the data objects are specified within the process, real-time process monitoring in terms

of Scorecards, Dashboards, and/or Cockpits.

✓ The support for Business Rules Modelling, in terms of business rules, rule script, flow rule, decision

table, report, and thereby decision-making support.

✓ The ability to run process ownership gap analysis, that is, to both process and processes rules or

process measurements.

We realize that many BPM teams wish the ability to relate process models to other vital aspects of enterprise

modelling, that is, business modelling, value modelling, performance management, and enterprise

architecture (e.g., business architecture, allocation/information systems architecture, and technology

architecture). The scope of BPMN does not provide such modelling capabilities, but a robust BPM modelling

environment could provide the linkages between the various BPM modelling domains (M von Rosing, 2015).

2.3 Decision Model and Notation (DMN) Complexity impairs the maintainability and understandability of conceptual models. Complexity metrics

have been used in software engineering and business process management (BPM) to capture the degree of

complexity of conceptual models. The recent introduction of the Decision Model and Notation (DMN)

standard provides opportunities to shift towards the Separation of Concerns paradigm when it comes to

modelling processes and decisions (F. Hasic, J. Vanthienen, 2019). DMN purpose is for:

✓ A common meta-model and notation for describing and modelling repeatable Business Decisions.

✓ Enables various groups to effectively collaborate in defining a Decision Model.

✓ Provides a standard notation for Decision Tables.

Page: 14

The following Table show us the special abilities and the specific fields that the three-notation language

engage.

BPMN CMMN DMN

Co

re

Co

nce

pts

Process Cases Decisions

Activities Events Rules

Focu

s Transitional Contextual Applied

Data Information Knowledge

Sem

anti

cs Procedural Declarative Functional

Token Event Condition Action First Order Logic

Table - 1 Scope of BPMS, CMMN and DMN

The next topic that it would be good to take a look is the mapping of Business Models to see an overview of the Enterprise Architecture Modelling Framework that covers general guidelines of all other business models analysis and applications for the whole enterprise system architecture design.

2.4 Business Models Mapping Business organization design as well as business information system implementation are expected to be

developed around the human needs and communication. Business people perceive economic phenomena

and interpret socio-economic environment in different ways. Each business organization has its own mission

and vision, organizational goals, value propositions, goods and services, distribution channels,

communication media, business stakeholders, roles and job descriptions. Therefore, the information

systems are different, although comparable, because of the implementation of similar pieces of software.

Taking into account the necessity to align ICT proposed solutions with business needs, business analysis

models and techniques are required to be considered and used for mapping the business model into an

information system for its further design and implementation in a particular environment. At first,

management science business models are shortly described and discussed. Furthermore, literature review

method is applied and its results are presented. The third part covers ArchiMate meta model combining

business models and presenting their mapping into diagrams in information modelling languages (i.e.,

SysML, UML) and Object Management Group (OMG) notation (i.e., BPMB, CMMN, DMN), (M. Pankowska,

2019).

There are five fundamental elements of each organizational structure, operating core encompassing the

members who provide products or services, the strategic apex including those stakeholders who determine

the behavior of others in the organization and represent it in its environment, the middle line to support the

coordination in case of lack of direct supervision, the technostructure determining the standards and

behaviors of others, and support staff aiding others in their daily duties.

Although business value is assumed by the product or business service provider, it is the recipient, who

determines whether or not value has been created. Business organizations do not deliver value, but they

can propose value by ensuring capabilities and circumstances for value beneficiary and deliver what is

expected. Presented in Fig.2, the Reference Model of Business is assumed to be considered as an Enterprise

Architecture modelling framework covering general guidelines of all other business models analysis and

applications for the whole enterprise system architecture design (M. Pankowska, 2019).

Page: 15

Although business value is assumed by the product or business service provider, it is the recipient, who

determines whether or not value has been created. Business organizations do not deliver value, but they

can propose value by ensuring capabilities and circumstances for value beneficiary and deliver what is

expected. In picture 5, presented the Reference Model of Business is assumed to be considered as an

Enterprise Architecture modelling framework covering general guidelines of all other business models

analysis and applications for the whole enterprise system architecture design (M. Pankowska, 2019). At the

following picture the Business Model Mapping.

Picture- 1 Business Models Mapping (M. Pankowska, 2019).

At the next chapter we will examine in depth the concept of Knowledge Intensive Process (KiPs). It is essential to

understudying KiPs because KiPs are the reason that made us to reconsider the existing process modelling approach.

Page: 16

3. Knowledge-intensive Processes (KiPs)

3.1 Knowledge-intensive Processes (KiPs) Definition

Knowledge-intensive Processes (KiPs) are the processes whose conduct and execution are heavily dependent on knowledge workers performing various interconnected knowledge intensive decision-making tasks. KiPs are genuinely knowledge, information and data centric and require substantial flexibility at design and run-time. (Vaculin R, Hull R, Heath T, Cochran C, Nigam A, Sukaviriya P, 2011). Business Process Management (BPM) environments provide wide support for different modelling styles and for all phases of the process life cycle. Process management approaches are often based on the assumption that processes are characterized by repeated tasks, which are performed on the basis of a process model prescribing the execution ow in its entireness. This kind of structured work includes mainly production and administrative processes. However, the current maturity of process management methodologies has led to the application of process-oriented approaches in new challenging knowledge-intensive scenarios, such as Smart Farming, healthcare, emergency management, projects coordination, case management, etc. In these working environments, most business functions involve collaborative features and unstructured processes that do not have the same level of predictability as the routine structured work. The need to deal with Knowledge-Intensive Process (KiPs) has emerged as a leading research topic in the BPM domain, due to the prominent role that knowledge workers play in modern organizations. This is backed by both quantitative considerations, as it has been estimated that today knowledge workers represent between 25% and 40% of the workforce (BPTrends, 2009), and qualitative observations, as knowledge workers have a major impact on organizational success and value creation. Several entities, ranging from public administrations to private companies, recognize that their core processes increasingly rely on best practices rather on explicit procedure-oriented processes. When knowledge creation, management and sharing are explicitly related to business processes, the collaborative nature of KiPs has to be considered as an integral part of practice-oriented processes. BPM researchers have recently recognized the need to extend existing approaches to support KiPs and meet their challenging requirements, which actual BPM frameworks are not able to handle adequately. Specifically, the knowledge and collaboration dimensions need to be integrated with the traditional control flow/data dimensions and consider them as a whole by possibly reshaping the process life cycle. Therefore, the ultimate goal of a BPM framework shifts from providing process automation to supporting decision making and collaboration between knowledge workers. This motivational framework has led the research community to bring together research areas that have been addressing related problems from different perspectives. On one side, the BPM community has largely focused on coordination support (relying on the foundational notion of process models), with minor emphasis of collaboration aspects. On the other side, the community targeting Computer Supported Cooperative Work (CSCW) has mainly focused on collaboration support, not necessarily framed in a process-oriented perspective. As knowledge work and KiPs combine coordination and collaboration with a knowledge dimension, the broad field of Knowledge Management (KM) has been considered too, as it allows to understand how knowledge is created, shared and used. KiPs has mainly focused on providing reference definitions for the concepts of “knowledge”, “knowledge workers” and “knowledge-intensity” for a business process. These definitional frameworks, often coupled with concrete use cases that illustrate knowledge work, are typically the starting point for the identification of some high-level characteristics that contribute to make a process knowledge intensive. While it is increasingly recognized that there is a lack of a holistic system support for knowledge workers and the processes they undertake, nowadays the discussion about KiPs misses a clear mapping between characteristics and system requirements. Case Management Model and Notation Language fill this gap. (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015).

Page: 17

3.2 Collaborative Knowledge Work (CKW) The establishment of a definitional framework for Collaborative Knowledge Work (CKW) represents the first step in the methodological approach adopted in (Mundbrod N, Kolb J, Reichert M, 2013). The authors rely on well-established and consolidated definitions of knowledge, knowledge work and knowledge workers, which allow them to define CKW as “knowledge work jointly performed by two or more knowledge workers in order to achieve a common business goal”. Four key characteristics of CKW are thus identified, namely

1. Uncertainty 2. Goal orientation 3. Emergence 4. Growing knowledge base.

The context-aware instantiation of a collaboration template (instantiation phase) results a collaboration instance that supports the run-time interaction between knowledge workers (collaboration run-time phase). Knowledge workers may access and exploit historical collaboration records as part of the knowledge base that supports instance progression. The actual collaboration instance, in turn, produces new collaboration records that are evaluated (records evaluation phase) to improve the understandings gained in the initial orientation and possibly reshape the defined templates.

Picture- 2 Collaborative knowledge work life cycle (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015)

Page: 18

3.3 Spectrum of Process Management

KiPs in the context of BPM, we classify business processes along a spectrum on the basis of the degree of structuring and predictability hey exhibit, which directly influence the level of automation, control and support that can be provided, as well as the degree of flexibility that is required.

Picture- 3 The spectrum of process management (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015)

At one end of the spectrum shown in Picture- 4 there are structured processes, which reflect highly predictable routine work with low flexibility requirements and controlled interactions among process participants (such as production and administrative processes) (Leymann F, Roller D, 2000). Process logic is known in advance and pre-definable, in terms of the activities to be executed, their dependencies, and the resources performing the activities. As a consequence, all possible options and decisions that can be made during process enactment are captured in a process model defined a priori, which can be repeatedly instantiated in a predictable and controlled manner (Mundbrod N, Kolb J, Reichert M, 2013). Structured processes with ad hoc exceptions have similar characteristics than structured processes, as they reflect operational activities that typically comply with a predefined plan. Although, the occurrence of external events and exceptions can make the structure of the process less rigid. The actual course of action may deviate from the predefined reference work practices and process adaptation strategies may be required (La Rosa M, Mendling J, 2008). In the presence of anticipated exceptions, possible deviations that can be encountered are predictable and defined in advance via exception handlers, typically prespecified into the process model. Conversely, unanticipated exceptions can be only detected during the execution of a process instance. Their handling typically requires ad-hoc process changes at run-time (Reichert M, Rinderle S, Kreher U, Dadam P, 2005). In many application domains, in the handling of insurance claims, work practices are rather unstructured and proceed on an ad-hoc basis. In unstructured processes with predefined segments the overall process logic is not explicitly defined, but the existence of policies and regulations allows to identify pre-definable, structured fragments. These fragments can refer to explicit, prescriptive procedures, or may take the form of underspecified templates and guidelines. Process parts that are undefined or uncertain can only be specified and incorporated in the range of the existing process model as the process evolves, and decisions regarding the specification of the process have to be deferred. Similarly, predefined process fragments need to be selected and properly composed on a per-case basis. A wide range of processes exhibit a loosely structured behavior. While work practices are not subject to

Page: 19

prescriptive reference procedures, the existence of policies and business rules induces constraints that implicitly the specification of the process have to be deferred. Similarly, predefined process fragments need to be selected and properly composed on a per-case basis. A wide range of processes exhibit a loosely structured behavior. While work practices are not subject to prescriptive reference procedures, the existence of policies and business rules induces constraints that implicitly frame the scope of action of process participants. The set of possible activities may be known and predefined, but their execution ordering is not entirely foreseeable, as many possible execution alternatives are allowed. Rather than using a procedural language for expressing the allowed sequences of activities, processes are described through the usage of constraints, that implicitly define these alternatives by prohibiting undesired execution behavior. Finally, the spectrum ends with unstructured processes, characterized by a low level of predictability and high flexibility requirements. Process participants decide on the activities to be executed as well as their execution order, and the structure of a process thus dynamically evolves. These processes directly reflect knowledge work and collaboration activities driven by rules and events, for which no predefined models can be specified and little automation can be provided. Knowledge workers rely on their experience to perform ad-hoc tasks on a per-case basis and handle unexpected changes in the operational context. For processes with these characteristics, only their goal is known a priory. The class of KiPs is transversal with respect to the classification presented here. Although the knowledge intensity generally increases along the spectrum, almost all the classes of processes discussed above may include elements that make them knowledge-intensive. The knowledge dimension may emerge, for example, in the way knowledge workers deal with unexpected exceptions. Similarly, knowledge workers put in place their experience and expertise for instantiating and concretizing underspecified procedures, or for contextually selecting and composing appropriate plan fragments. Moreover, individual and collaborative decision-making contributes to the definition of the best course of action in loosely structured or unstructured work practices. At the following pages we will examine in depth the main characteristics of KiPs.

3.4 Main Characteristics of Knowledge-Intensive Process (KiPs) KiPs are inherently people-centric, as they are mainly performed by knowledge workers, autonomous decision makers with different backgrounds, expertise and experience (Davenport TH, 2005). Knowledge workers create, access, update and exploit different types of domain-specific knowledge to achieve intended goals performing activities that require decision making capabilities.

Eight most representative key-characteristics of KiPs

C1 Knowledge-driven: The status and availability of data and knowledge objects drive human decision making and directly influence the flow of process actions and events. Process-related knowledge evolves as a result of process progression and the occurrence of contextual events (Marjanovic O, Freeze R, 2011). Explicit knowledge can be formalized and encoded in some form of knowledge base, so as to define knowledge objects, data, information and artifacts to be considered as part of process context and execution state. Implicit or tacit knowledge is linked to the capabilities and experience of process participants and is embedded in their work practices and decision choices (Gronau N, Muller C, Uslar M, 2004).

C2 Collaboration-Oriented: Process creation, management and execution occurs in a collaborative multiuser environment, where human-centered and process related knowledge is created by collaboration, shared and transferred by and among process participants with different roles. Process progression and completion often require a team-based approach. It depends on knowledge flows and transfers of data and knowledge objects between communicating process participants (BPTrends, 2009), (Marjanovic O, Freeze R, 2011), (Marjanovic O, Skaf-Molli H, Molli P, Godart C, 2007).

Page: 20

C3 Unpredictable: The exact activity, event and knowledge ow depends on situation and context specific elements that may not be known a priori, may change during process execution, and may vary over different process cases. The knowledge worker is often not able to predetermine the overall process structure in terms of the activities to be executed and their ordering, the data and knowledge sources to be exploited and the roles and resources required for process progression and completion (Mundbrod N, Kolb J, Reichert M, 2013), (Reichert M, 2011), (Swenson KD, 2010).

C4 Emergent: The actual course of actions gradually emerges during process execution and is determined step by step, when more information is available. Process participants continuously assess process progression and then act or plan the actions to be performed, depending on the process status and the available data and knowledge elements (Mundbrod N, Kolb J, Reichert M, 2013). Each performed action and taken decision towards the achievement of a given goal has the effect of producing knowledge. It will be exploited for supporting subsequent decisions and determining the next goals to be achieved as well as the actions to execute (Reichert M, Weber B, 2012), (Swenson KD, 2010).

C5 Goal-oriented: The process evolves through a series of intermediate goals or milestones to be achieved. These goals may be known a priori and predefined, or gradually defined as the result of acquired knowledge and previously achieved goals (BPTrends, 2009), (Marjanovic O, Freeze R, 2011).

C6 Event-driven: Process progression is affected by the occurrence of different kinds of events that influence knowledge workers' decision making. During process execution, process participants may have to react to different kinds of events, which can occur in any sequence. These events represent changes that affect process state, process-related data and knowledge, and process execution context and environment (BPTrends, 2009). Changes in the process data as well as events related to the initiation and completion of activities may correspond to the achievement of process goals and may act as triggers for subsequent decision-action steps (Davenport TH, 2005). Contextual changes require to properly adapt and modify process behavior.

C7 Constraint and Rule-driven: Process participants may be influenced by or may have to comply with constraints and rules that drive actions performance and decision making. Being a form of knowledge, rules and constraints can be either explicit and available in guidelines, policies and other sources of business rules, or implicit and thus embedded in participants' personal work practices (Dalmaris P, Tsui E, Hall B, Smith B, 2007). Rules and constraints contribute to the definition of decision criteria and may act as eligibility paradigms for electing the actions to be executed, as well as the knowledge and data sources to be exploited (Reichert M, 2011). The structuredness tying the high flexibility of artful processes stems indeed from the need to comply to given constraints: for instance, the writing of a deliverable in a research project must end before that the deadline for its submission expires.

C8 Non repeatable: The process instance undertaken to deal with a specific case or situation is hardly repeatable, different executions of the process vary from one another. Emergency response plans, for example, are usually unique, as they reflect processes to be applied in a specific emergency situation. However, this does not exclude the possibility of predefining process fragments and templates to be selected and re-used in a context-dependent way. In addition, mining activities performed over the history of executed processes may contribute to the identification of action/event patterns and declarative knowledge (e.g., rules and constraints), which could be exploited to refine existing work practices and policies. Furthermore, it would foster the reuse of best practices and guidelines, and convert tacit knowledge in explicit knowledge objects (Isik O, Van den Bergh J, Mertens W, 2012).

Page: 21

3.5 General Requirements for KiPs The analysis of real-world scenarios and the systematization of KiPs characteristics enable to identify the fundamental components of a collaborative KiP, as well as their interdependencies (Picture-5).

Picture- 4 Fundamental components of a KiP (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015)

At the core level, it is possible to identify a tight integration of data & knowledge elements with knowledge actions. These components mutually influence each other: knowledge actions rely on the availability and content of data & knowledge elements, which in turn are affected by the performance of knowledge actions. The relations between different data & knowledge elements induce an information model that enables the ow of information to support actions' performance and decision making. This data-centric perspective emphasizes the need to capture and manage the structure, interactions and behavior of data & knowledge elements. The intra and inter-dependencies between data & knowledge elements and knowledge actions are influenced and framed by rules and constraints, often related to guidelines and best practices. In particular, rules and constraints can define data and execution dependencies on knowledge actions and dictate their mandatory/optional nature. Similarly, they can express the aforementioned dependencies of knowledge tasks on data & knowledge elements, the impact of knowledge actions on the information model, and the effects of events and user decisions on the overall process structure. All the elements introduced so far directly relate to the specific goals to be achieved. Goals are mainly defined by knowledge workers and are gradually achieved as a result of actions' performance and data & knowledge evolution. The complex interdependencies among all these elements induce an overall coordination structure, coupled with the collaboration structure of knowledge workers. Both the coordination and collaboration structures dynamically change in relation to the actual context and environment, which impacts on goals, actions, data & knowledge elements and their interdependencies. The identification of KiP components suggests that, in order to enable process-aware system support, the different interrelated elements have to be captured and managed along all the phases of the life cycle (Picture-5). Therefore, we categorize the key requirements for KiPs support into seven classes, that reflect the main components identified before. Moreover, according to the dynamics of the life cycle, the requirements in each category reflect the need to support the definition, evolution, monitoring and analysis of the corresponding component.

Data

R1 Data modelling: An information model including all relevant data manipulated by the process and their interrelationships is required. Data can be more or less accurate, and may refer to different levels of abstraction, ranging from detailed properties provided by process variables to more aggregate information stored in data objects, which hold information structures pertinent to the global context.

Page: 22

R2 Late Data Modelling: The arising of new knowledge at run-time may involve the creation/modification of new/existing data. Therefore, a knowledge worker must be allowed to add new data to the information model during the process enactment, or to alter or remove the existing ones. R3 Access to appropriate data: All relevant data must be accessible at any point of the process enactment to

those participants having the required authorizations, not only during the execution of a specific action.

R4 Synchronized access to shared data: Different tasks/users may access and modify the same data concurrently at the same time, without the risk of affecting the integrity of data. The consistency of data must be maintained during the process enactment.

Knowledge Actions

R5 Represent data-driven actions: A KiP is characterized by actions whose enactment significantly depends on the evolution of the information model, so that purely data-driven process progression can be supported. It is therefore required that knowledge actions are enriched with constraints defined on process data, stating how data may constrain the action execution or may be affected after an action completion. R6 Late actions modelling: To deal with the “emergent nature” of a KiP, users must be allowed to add new knowledge actions to the process instance during its enactment, or to alter the existing ones.

Rules And Constraints

R7 Formalize rules and constraints: In a KiP, the existence of policies, rules and regulations can influence the process structure and constrain its execution. To this end, a user must be allowed to explicitly define constraints or business rules on process data. R8 Late constraints formalization: When new data or actions emerge during process enactment, a knowledge worker must be allowed to add new constraints at run-time, or to alter the existing ones.

Goals

R9 Goals Modelling: For a KiP, concrete goals may be created and their achievement may be associated to the result of acquired knowledge, when one or more data & knowledge elements assume a specific value determined by knowledge workers. Therefore, a mechanism for representing one or more process goals defined on data and knowledge element is required. R10 Late Goal Modelling: During process enactment, new process goals may arise as a result of knowledge workers' decisions or due to the evolution of data & knowledge elements. A knowledge worker must be allowed to associate new goals to a running process or to alter/remove existing goals that have become outdated.

Processes

R11 Support for different modelling styles: A KiP can be seen as a combination of knowledge entities (data, actions, etc.) having different degrees of structuredness. To possibly model any kind of KiPs schema, it is required to provide the ability to select and combine various modelling alternatives. R12 Visibility of the process knowledge: An aggregated perspective of data, actions, constraints and goals involved in a running process must be provided, including their state as well as their interdependencies. R13 Flexible process execution: A KiP is not dictated ahead of time but emerges as part of the collaboration and negotiation between the participants, which can decide to change the order of steps in the process and the type of information needed. A knowledge worker must be able to \step back" or \jump forward", to re-execute previously performed actions, or to skip actions deemed unnecessary in a given instance. R14 Deal with unanticipated executions: A KiP is executed in environments that may change in unpredictable ways during its execution. The presence of unanticipated exceptions reflecting environmental changes or unexpected actions outcomes is common during a KiPs enactment. Hence, it is required to catch

Page: 23

unanticipated exceptions and provide mechanisms to generate the recovery procedure dealing with such exceptions, which are either manual or completely automated, depending on the specific case. R15 Migration of process instances: A KiP is often associated to environments, data and actions that evolve over time (due to changes in the business, in the technological environment, etc.). To maintain the running instances of a KiP aligned with the real-world specifications that emerge at run-time, the migration of process instances into models compliant with new specifications is crucial to support the execution of a KiP.

R16 Learning from event logs: A KiP must help an organization to learn from previous executed instances/cases. Therefore, it is required to record event logs that trace the process progression and to provide mechanisms for discovering or improving the structure of a KiP, starting from the knowledge gathered from such logs. A learning activity based on event logs may help to understand the impact of a KiP in real world, discover the KiP's process model, or check whether a pre-specified model is conformant with the event logs. It may also result in an improvement of the information model, in the definition of new actions, etc. R17 Learning from data sources: The enactment of KiPs may have not been supported by a PMS in the past. However, there could be data reporting or tracing the execution of KiP, even though not formatted as event logs. Such data could consist in unstructured texts such as PDF documents, semi-structured texts such as email messages, structured texts such as CSV files, database entries, etc. In these circumstances, there could not be a direct match between the fulfillment of an activity and a record in a list of events, such as logs. Nonetheless, the capability of learning from the past should be guaranteed anyway. Therefore, it is required to gather knowledge from heterogeneous data sources, in order to discover or improve the structure of a KiP.

Knowledge Workers

R18 Knowledge workers’ modelling: The ability to define a resource model including multiple participants with multiple roles/capabilities is fundamental for KiPs. Roles serve as a means of grouping knowledge workers with similar duties. Capabilities are used for specifying whether a knowledge worker provides the required skills to execute a specific action. R19 Formalize interaction between knowledge workers: During the lifetime of a KiP, there is a range of involved knowledge workers who play different roles and collaborate during the process enactment. To this end, mechanisms for defining structured or unstructured protocols that allow knowledge workers to communicate and collaborate are required. R20 Define knowledge workers’ privileges: It is required to define explicitly knowledge workers’ privileges for specifying permissions for creating/altering/deleting data and knowledge elements, avoiding that confidential information is made available to inappropriate knowledge workers. R21 Late knowledge workers’ modelling: Given the “emergent” nature of a KiP, it could be required to insert new knowledge workers and their respective capabilities to the resource model at run-time, to alter capabilities of existing knowledge workers or to remove existing knowledge workers from the resource model. R22 Late privilege modelling: At run-time, it may be required to add/remove/alter privileges associated to existing knowledge workers, since new knowledge entities may arise during the KiP enactment. R23 Capture knowledge workers’ decision: At run-time, decisions made by knowledge workers may affect the process progression or the state of information model. To this extent, it is required to capture knowledge workers' decisions at run-time and to associate their occurrence's impact on the process progression and on the information model.

Environment

R24 Capture and model external events: An external event is a trigger coming from the environment that changes the state of the running process, by altering the value of data in the information model. Hence, it is

Page: 24

required to allow to explicitly represent external events coming from the environment and to associate their occurrence's impact on the information model. R25 External events late modelling: During process enactment, if a new external event (that was not previously captured) occurs, a knowledge worker must be allowed to formalize it and to associate its occurrence impact on the information model. The following Table 2 shows, for each requirement, the subset of characteristics relevant for the requirements.

Requirement Characteristics Mainly Included

R1 Data modelling C1 Knowledge-driven C2 Collaboration-Oriented C7 Constraint and Rule-driven

R2 Late Data Modelling C1 Knowledge-driven C2 Collaboration-Oriented C4 Emergent C7 Constraint and Rule-driven

R3 Access to appropriate data C1 Knowledge-driven C2 Collaboration-Oriented C7 Constraint and Rule-driven

R4 Synchronized access to shared data C1 Knowledge-driven C2 Collaboration-Oriented C3 Unpredictable

R5 Represent data-driven actions C1 Knowledge-driven C3 Unpredictable C7 Constraint and Rule-driven

R6 Late actions modelling C3 Unpredictable C4 Emergent

R7 Formalize rules and constraints C7 Constraint and Rule-driven

R8 Late constraints formalization C3 Unpredictable C4 Emergent C7 Constraint and Rule-driven

R9 Goals Modelling C5 Goal-oriented

R10 Late Goal Modelling C3 Unpredictable C4 Emergent C5 Goal-oriented

R11 Support for different modelling styles C1 Knowledge-driven C5 Goal-oriented C6 Event-driven C7 Constraint and Rule-driven

R12 Visibility of the process knowledge C1 Knowledge-driven C2 Collaboration-Oriented C5 Goal-oriented C7 Constraint and Rule-driven

R13 Flexible process execution C3 Unpredictable C4 Emergent

R14 Deal with unanticipated executions C3 Unpredictable C4 Emergent C6 Event-driven

R15 Migration of process instances C2 Collaboration-Oriented

Page: 25

Requirement Characteristics Mainly Included

C4 Emergent C6 Event-driven C8 Non repeatable

R16 Learning from event logs C1 Knowledge-driven C4 Emergent C8 Non repeatable

R17 Learning from data sources C1 Knowledge-driven C4 Emergent C8 Non repeatable

R18 Knowledge workers’ modelling C1 Knowledge-driven C2 Collaboration-Oriented

R19 Formalize interaction between knowledge workers

C2 Collaboration-Oriented

R20 Define knowledge workers’ privileges C1 Knowledge-driven C2 Collaboration-Oriented

R21 Late knowledge workers’ modelling C1 Knowledge-driven C2 Collaboration-Oriented C4 Emergent

R22 Late privilege modelling C1 Knowledge-driven C2 Collaboration-Oriented C4 Emergent

R23 Capture knowledge workers’ decision C1 Knowledge-driven C2 Collaboration-Oriented C4 Emergent C7 Constraint and Rule-driven

R24 Capture and model external events C6 Event-driven

R25 External events late modelling C4 Emergent C6 Event-driven

Table - 2 KiPs Requirements and the Mainly Included Characteristics (Claudio Di Ciccio, Andrea Marrella, Alessandro Russo, 2015)

The characteristics and requirements of KiPs force to reconsider the classical process life cycle based on the design, execute and monitor, analyze, re-design sequential steps. The boundary between process design and execution gradually disappears, replaced by a continuous interleaving and overlapping between design, execution and adaptation activities. Although it is possible to foresee the use of templates and fragments as collections of predefined elements to be composed at run-time, in an extreme case the process is completely built from scratch while it is executed, or it has to be discovered by analyzing existing work practices. Initial research efforts show that data-centric approaches represent a promising solution for supporting KiPs and case management practices. Although object-aware approaches and artifact-centric models at the heart of the CMMN standard can open the way for a new generation of flexible and adaptive case management systems, the level of maturity of existing prototypical frameworks is low if compared to consolidated Process Management Systems (PMSs). Consequently, the role of these emerging approaches, as well as the potential impact of the upcoming CMMN standard, clearly need further investigation to evaluate the related tools and methods in concrete settings. The advantages with respect to other consolidated approaches have to be verified as well.

Page: 26

Picture- 5 Generic framework for the structuring of KiPs with case management, (Mike A. Marin, Matheus Hauder, Florian Matthes, 2015).

3.6 Case Management: Evaluation for Knowledge-Intensive Processes Today's work environments require highly trained experts that are able to perform many tasks

autonomously. These experts are referred to as knowledge workers and their processes have a huge impact

on the success of an organization, Knowledge-intensive processes (KiPs) integrate data in the execution of

processes and require substantial amount of flexibility at run-time. Due to the lower level of predictability

compared to routine processes, KiPs need to balance between structured elements for repetitive aspects

and unstructured elements to allow creative solutions for complex problems. In addition to this uncertainty

during the definition of the process, KiPs can also be characterized as goal oriented, emergent, and

knowledge creating. Process models for highly structured routine processes are not suitable for KiPs since

they would become too complex to manage and maintain. KiPs require more emphasis on collaboration and

creativity compared to highly structured processes, (Mike A. Marin, Matheus Hauder, Florian Matthes,

2015).

Based on initial ideas on case handling numerous other approaches have been proposed, Case Management, Adaptive Case Management, Dynamic Case Management, Production Case Management, and Emerging Case Management. Every case management approach covers various aspects that have the same main purpose of case instances, to manage relevant data and actions that are processed by case workers to achieve a certain goal. Case templates capture best practice knowledge that can be reused in a particular context. Main difference between existing case management approaches is their support for the adaptation and instantiation directions that are marked with (a) and (b). In (b) case templates are predefined through case modelers. Alternatively, or in addition to modelling, case templates emerge through adaptations of case workers in (a). Both directions can be integrated to support the entire knowledge work lifecycle presented in, (Mike A. Marin, Matheus Hauder, Florian Matthes, 2015).

Page: 27

4. Case Management Model and Notation (CMMN)

4.1 CMMN Standard

The focus of the Case Management Model and Notation (CMMN) specification is the notation, the meta-model, the interoperability between tools, and minimum execution semantics (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). The main objective of CMMN is to define a common meta-model and notation for modelling and graphically expressing a Case. A Case involves actions taken regarding a subject in a particular situation to achieve a desired outcome. The subject of a case may be a person, a legal action, a business transaction, or some other focal point around which actions are taken to achieve an objective. The situation commonly includes data that inform and drive the actions taken in a case. (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). There are two phases for each executable case. First, during design-time, business analysts prepare the case execution by modelling the case. Once a case has started to being executed, the case is in run-time. In this phase, the case workers are working on achieving the case objectives (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). Case management is concerned with determination of which tasks are applicable, or which follow-up (discretionary) tasks are required, given the state of the case. Decisions and flow may be controlled by events or new facts that continuously emerge during the course of the case, such as the receipt of new documents, completion of certain tasks, or achieving certain Milestones. Individual tasks that are planned and executed in the context of the case might be predefined procedural processes in themselves, but the overall case cannot be orchestrated by a predefined sequence of tasks. Finally, the meta-model and notation are used to express a case model in a common notation for a particular type of cases, and the resulting model can subsequently be instantiated for the handling of a particular instance of a case (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). CMMN is currently considered an alternative-to-BPMN language (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). Several applications of Case Management, can be identified, including among others, patient treatment and medical diagnosis in healthcare, mortgage processing in banking and application and claim processing in insurance (I. Routis, M. Nikolaidou, D. Anagnostopoulos, 2020). Very few of these applications can be found in the literature that use CMMN in real-world scenarios. The focus of the CMMN specification is the notation, the meta-model, interoperability between tools, and minimum execution semantics. Aspects like user interface, facilities for end users, user interaction with a case, tool usability, and others are considered implementation details and outside the scope of the specification, (Mike A. Marin, Matheus Hauder, Florian Matthes, 2015).

Modelling characteristics and requirements are those that a modelling tool should expose to support the ability of a case modeler to create case templates that satisfy knowledge-intensive process. Case worker environment characteristics and requirements are those that an implementation should expose for case workers interacting with a case instance to support knowledge-intensive process. Although, CMMN does not provides implementation details for the execution environment, CMMN defines several levels of compliance with the specification, (Mike A. Marin, Matheus Hauder, Florian Matthes, 2015). Business analysts and software developers are oriented towards process and decision modelling, but the

CMMN language is the best suitable for nonprocedural modelling in situational management cases.

Page: 28

4.2 CMMN Elements, Notation and Description

Name CMMN Notation Description

Case Plan Item

A case plan item contains all the case elements that are involved representing the content of the case as well as the way to process and resolve the case.

Task Item

Tasks describe activities that can be executed during the run-time phase. Four types of tasks are supported: human (performed by a knowledge worker), process (to embed a process), decision (to embed a decision) and case (to embed other cases).

Human Blocking Task Item

A human task is used to model work that needs to be done by a human actor. Human blocking: a task executed by a knowledge/case worker. This task can only be completed once all the work related to it has been done.

Human non-Blocking Task Item

A human task is used to model work that needs to be done by a human actor. Human non-blocking: a task executed by a knowledge/case worker. This task is 'completed' as soon as the work has been initiated.

Decision Task

A decision task can be used to invoke a Decision Model and Notation (DMN) decision from a case. A decision task is a regular task that requires a decision reference attribute that references a decision definition by its key.

Process Task

A process task can be used to invoke a BPMN process from a case. A process task is a regular task that requires an attribute process reference which references a process definition by its key.

Case Task

A case task can be used to call another CMMN case. A case task is a regular task that requires a case reference attribute that references a case definition by its key.

Discretionary Items

These identify a task item (all task type can be defined as discretionary), of which instances can be planned, to the discretion of a case manager.

Stage Plan Item

Stages are logical containers of tasks to be performed within the course of a case. They allow structuring a case hierarchically.

Discretionary Stage Plan Item

These identify a stage item, of which instances can be planned, to the discretion of a case manager.

Discretionary Plan Flagment Item

A set of plan items, possible depend on each other, and that often in case plans in combination, representing a pattern.

Page: 29

Name CMMN Notation Description

Case File Item

A Case File Item represents a piece of information of any nature, ranging from unstructured to structured, and from simple to complex. In knowledge-intensive work, documents are typical outputs of tasks or stages.

Milestone Plan Item

A Milestone represents an achievable target, defined to enable evaluation of progress of the case. No work is directly associated with a Milestone, but completion of set of tasks typically leads to achieving a Milestone of the case (!: Required Rule, #: Repetition Rule).

Event Listeners

An Event Listener captures events (general events, timer events, user events), which are things that happen during a case. Events may trigger, for example, the enabling, activation, and termination of stages and tasks, or the achievement of milestones.

Sentries

Sentries allow defining logical dependencies between tasks and/or events, watching out for important situations to occur. Sentries also represent a combination of conditions and events that define the sequence of tasks to be implemented.

Markers Manual Activation Rule

Required Rule

Auto Complete # Repetition Rule

Table - 3 CMMN Elements, Notation and Description.

The next chapter present the evolution of Farm Management Information Systems and recognizes that the process

of FMIS are highly data driven, event driven and are strongly depended on human control and experience.

Page: 30

4.3 Farm Management Information Systems Farm Management Information Systems (FMIS) have evolved from simple record keeping to advanced

solutions capable of capturing new trends involving spatial and temporal management, distributed sensors

including interoperability detectors, future web applications and internet services.

FMIS was originally designed to treat the farmer as the main focus of the system, given that now data flow

to and from the tractor board and connections to other pieces of equipment, such as precision farming

devices, can be managed through an FMIS. This evolution led to the integration of a rich set of functions and

opened up the possibility of improving farm cost control.

The tremendous advances in technological advances in computers and electronics in agriculture in recent

decades have brought about significant changes in the working environment for the rural community. This

has created a huge amount of data used by farmers and the challenge is to make the best use of this data to

make useful and practical farming information available. The farm manager today has to choose between

different technology providers and data providers using the most appropriate information to make the best

decisions for his farm (Soren Marcus Pedersen, Kim Martin Lind, 2017).

Decision making is a critical element for farmers and many researchers have studied it in relation to the

availability of data provision (Fountas S, Wulfsohn D, Blackmore S, Jacobsen HL, Pedersen SM, 2006).

The most important aspect of conducting farm research on management decisions is to understand farmers'

tacit knowledge and how farmers react when a decision needs to be made (Gladwin H (1989) ).

This is an important direction that researchers working with data management in agriculture should continue

to provide farmers with the information they need to improve decision-making at specific stages of their

production process.

The basis for effective decision making is the availability of high-quality data. In Europe, most farms have

difficulty using available data and information sources, which are fragmented, fragmented, difficult and time

consuming to use. This shows that the full potential of this data and information is not well used by farmers.

The integration of historical data, real-time data from various agricultural sources, knowledge sources, and

compliance with standards, environmental guidelines and financial models into a coherent management

information system is what is expected to correct this situation (Fountas S, Ess D, Sorensen CG, Hawkins S,

Blumhoff G, Blackmore S, Lowenberg-DeBoer J, 2005).

Farm Management Information Systems (FMIS) have evolved from simple record keeping systems to large

and complex systems in response to the need for communication and data transfer between databases to

meet the requirements of different stakeholders. FMIS are electronic data collection and processing tools

for providing information with potential value in management decision making (Boehlje MD, Eidman VR,

1984).

In a more detailed expression, FMIS is defined as a programmed system for collecting, processing, storing

and disseminating data in the form required to perform operations and operations (Sorensen GC, Fountas S,

Nash E, Pesonen L, Bochtis D, Pedersen SM, Basso B, Blackmore SB, 2010). Key FMIS elements include specific

farmer-oriented designs, user-specific environments, automated data processing functions, expert

knowledge and user preferences, standardized communication data and scalability are all provided at

affordable prices to farmers (Murakami E, Saraiva AM, Ribeiro Junior LCM, Cugnasca CE, Hirakawa AR, Correa

PLP, 2007).

FMIS has become a specialty through the integration of new technologies, such as web applications and

applications for smartphones (Nikkila R, Seilonen I, Koskinenet K, 2010).

Page: 31

Their study found that commercial applications mostly deal with data processing for day-to-day agricultural

activities, while academics continue to explore new horizons in high-complexity research, recording new

trends involving spatial and temporal management, distributed systems that include interoperability of

detection devices, future internet components and services. Commercial applications tend to focus on

solving day-to-day operations by aiming to generate income for farmers through better resource

management and on-site business planning. Advances required for the development of FMIS include

technology improvements, adaptation incentives, specific new features, and a greater emphasis on usability

and human-design software interacting with the computer. Dissemination of information management as a

business, innovation in the rural community could benefit from the comprehensive research developed in

recent decades on the adoption of Information and Communication. Technologies (ICT) and e-commerce

between consumers and small businesses.

Agriculture is a complex system that incorporates a range of interactions between farmers, advisors, traders,

government agencies, agricultural machinery, environmental regulations, economic assessments and more.

This system is summarized in Picture 1 which shows in addition to the interactions, concerns and conflicts

between the various entities, where the farm manager is in the middle of the proposed system (Sorensen et

al. 2010).

FMIS can cover a large number of functions, such as inventory, calendar, direct sales, and specific

management locations. A set of 10 functions was presented by (Fountas S, Carli C, Sorensen CG, Tsiropoulos

Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B, 2015a) and is given in Table 1.

Function Title Function Description

Field Operation Management Recording of agricultural activities to help the farmer optimize crop production by planning activities and monitoring the actual execution of planned work. Precautionary measures can be taken based on the data monitored.

Best Practice (including yield estimation)

Production task and methods related to the implementation of best practices in accordance with agricultural standards (e.g., biological standards, integrated crop management (ICM)). A performance estimate is possible by comparing actual requirements and alternatives to hypothetical best practice scenarios.

Finance Estimation of the cost of each agricultural activity, calculations of inputs-outputs, equipment charges, labor requirements per unit area. It is displayed and the real costs are also compared and introduced in the final evaluation of the financial viability of the farm.

Inventory Monitoring and management of all production materials, equipment, chemicals, fertilizers and sowing and planting materials. The quantities adapted according to the plans and orders of the farmers.

Traceability Cut recall, using an authentication system to control the production of each production department, including the use of imports, staff and equipment, which can be easily archived for quick recall.

Reporting Creating breeding reports such as planning and management, work progress, worksheets and instructions, order purchases, cost reporting and plant information.

Sales Manage orders, service charges and online sales.

Machinery Management Includes equipment usage details, average cost per hour worked or per unit area. It also includes fleet management and logistics.

Page: 32

Human Resources Management

Employee management, availability of employees in time and space, management of working time, pay, qualifications, training, performance and specialization.

Table - 4 Farm Management Information Systems (Fountas S, Carli C, Sorensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M, Wiebensohn J, Tisserye B, 2015a)

In addition to anthropocentric FMIS, there has also been a significant technological breakthrough in in-

vehicle tractor performance innovation that allows tractor acquisition and application of status data via

ISOBUS (Universal Electronic Communication Protocol between Tools, Tractors and Computers) and provides

useful information in optimizing overall operations and field productivity (Backman J, Oksanen T, Visala A,

2013). These systems are based on tractors and accurate GPS systems where they appear as standard

features on modern tractors with the aim of providing improved operation and management function

through the use of extensive databases as a basis for decision support and control actions. In addition, the

development of autonomous vehicles adopted on-site work will gradually change the role of tractor operator

monitoring and strategic management, as this development will require an explicit information management

system capable of managing interactive information flows and providing useful real-time guidance for the

execution of functions (Tsiropoulos Z, Fountas S, Gemtos T, Gravalos I, Paraforos D, 2013b). The link between

ISOBUS and precision agriculture, the innovations will meet the requirements of the farm manager, opening

up a wealth of information for improved crop production management. For the tractor in the middle of an

information system, a change of perspective from the farmer or farm manager to the core of the system, to

a tractor-centric approach leading to an innovative FMIS architecture where information flows intelligent

machine entity that has an upgraded role as part of the decision-making process was introduced by (Fountas

S, Sorensen CG, Tsiropoulos Z, Cavalaris C, Liakos V, Gemtos T, 2015b). The term Farm Information

Management System (FMMIS) was used to describe the above approach, which is based on on-site action

information retrieval procedures illustrated in Picture 2.

However, there is not always a smooth path to commercial availability even for systems that have already

shown their potential in a research environment. In a single country, the Netherlands, for example, many

trade initiatives to develop a GIS platform for use in agriculture have failed.

Picture- 6 Farm Management Information System (Sorensen et al. 2010)

Page: 33

A system called "Akkerweb" (English: Farm Maps; www.akkerweb.nl) is currently the most gaining credibility.

Akkerweb is the product of a public-private partnership between Agrifirm, the largest agricultural

cooperative in the Netherlands, and Wageningen UR, the leading agricultural research organization in the

Netherlands. Akkerweb is a geo-information system platform that allows the acquisition of geographic data,

management, visualization and use at the exploitation level in conjunction with an FMIS standard

(Kempenaar C, van Evert FK, Been T, Kocks CG, Westerduk CE, 2016). In addition, farm consultants can access

the data if the farmer wants to share data. Akkerweb offers GIS functions and a number of generally free

applications ("applications"), such as a crop program application, a satellite data application, and a sensor

data application for visualizing and analyzing soil and crop data, and for creating job maps. Akkerweb also

contains many subscription-based applications for the application of variable rates of pesticides and

fertilizers. Akkerweb's success is due to the combination of its ICT infrastructure and scientific content,

bottom-up development with users in the driver's seat and effective collaboration between an agricultural

cooperative, a research institution and an IT company with sufficient resources to the construction of the

required infrastructure. Akkerweb is an open platform that provides the feeling that third parties can also

use the Akkerweb platform to develop and offer paid services. Today, data from approximately 30,000 crops

are stored using Akkerweb.

There are of course many other commercial FMIS in Europe and around this world used by farmers or

farmers' cooperatives. One successful system is FARMSTAR in France (Farmstar, n.d.), a technology-based

satellite service developed and delivered by Airbus Defense and Space since 2003. FARMSTAR users receive

advice on by managing precision agricultural products knowing the exact time and area where they need to

apply fertilizers and pesticides. Satellites flying over the fields receive accurate measurements of irradiated

solar energy absorbed and reflected from the surface throughout the farm. The value of reflected energy

varies depending on the level of vegetation growth, so satellite measurements can indicate such as soil

moisture, surface temperature, leaf cover and chlorophyll level. Personalized "suggestion cards" divided into

very small areas of the field are provided to each user, offering recipes for the necessary amounts of

chemicals to be applied, as well as where and when to apply. FARMSTAR, the service provides its subscribers

with the opportunity for a better environment, financial and social management.

Picture- 7 Farm Machinery Management Information System (Sorensen et al. 2010)

Page: 34

The process of adopting technological innovations in agriculture is very complicated because it is influenced

by a wide range of factors and guides that could influence the decision to approve or reject innovation.

Behavioral attitude, education and awareness, cultural background and rules, social influences, economic

and financial variables, policy and market conditions can serve as explanatory variables for the adoption of

innovation standards, along with structural and infrastructure factors, infrastructure, the characteristics of

innovation itself (Daberkow SG, McBride WD, 2003).

At the next chapter we will see how CMMN can empower FMIS process and helps to model unexpected,

emergent, event driven process throw a case study of a Smart Farming Green House System.

Page: 35

4.4 CMMN Empowers Smart Farming Processes Smart Farming processes are processes that are strongly depending on the experience and the knowledge

of their workers, are data driven and event driven, CMMN is the language that unlock and give complete

access to all the vital information concerning the case and giving them discretion and control on how a case

evolves.

FMIS case management is a type of business process technology that does not use control flow to describe

the process. The case (case file or case folder) is the main concept, and it contains all the data and

information about the process. FMIS case management is about empowering workers by providing them

with access to all the information concerning the case and giving them discretion and control on how a case

evolves. FMIS case management is not about the process, is about the workers. In a traditional workflow or

process system the designer encodes the business goal to be accomplished in the model. Thereafter the

system is responsible for the business goal and it uses the workers to achieve that goal. In a FMIS case

management system, on the other hand, the knowledge workers are responsible for the business goal and

they use the system as a tool to accomplish that goal. That it is why FMIS case management relies more in

the knowledge worker’s judgment than in control flow.

In order of understudying better how essential it is to model an intensive knowledge process by Case

Management Model and Notation language (CMMN) we will present a case study of a Smart Farming process

bellow and we will make a model that will make clear how an unstructured process can be organized and

how CMMN can orchestrate BPMS and DMN.

5. Smart Farming Case Study

5.1 Case Study Description

“The smart farming case concerns the management of greenhouses. There is a ground drip irrigation system and the humidity in the green house should be controlled in order to prevent plant diseases related to the water provided. There is an opening rooftop, which the farmers open and close manually, based on the season, the weather and the farmer’s personal experience and instinct. In case of a “smart” management system of the greenhouse, we assume that sensors are installed in the greenhouse to track humidity and temperature both in the soil and the air. The sensed data reflecting the conditions in the greenhouse can, in turn, trigger the automatic opening or close of the rooftop in order to always ensure that the conditions in the greenhouse fit the plants’ needs. The farmer could also take the final decision about the rooftop after the system sends him an open/ close suggestion that he accepts or rejects based on his experience and intuition. If there are critical conditions, e.g. a forthcoming storm, the farmer will make the final decision even if the system suggests otherwise. In Figure \ref{cmmnmodel} (εδώ θα πρέπει να κάνεις αναφορά στο μοντέλο που θα συμφωνήσουμε), we utilize the CMMN modelling language \cite{ObjectManagementGroup2016CaseV1.1} (εδώ θα πρέπει να κάνεις αναφορά στην γλωσσα CMMN) to graphically represent the main activities during the smart farming case where the greenhouse humidity is controlled by automating the control of the rooftop (open or close). Further, if there is an air purification system in the greenhouse, it can be activated automatically when the rooftop is closed. The sensed data in the greenhouse can enable more services offered by the smart management system, such as suggestions of when to water the plants and of the quantity of the water."

Page: 36

5.2 Smart Farming Case Study Model

Picture- 8 Smart Farming, Smart Management System of the Greenhouse (BPMN.io CMMN Editor/Viewer).

Page: 37

5.3 Smart Farming Model Description

Our model consists of four important Stage Plan Items which is the Irrigation System, Rooftop Management,

Watering Plants and the Air Purification System. We will try to describe how each of these segments work

and how collaborate each other in order to achieve the best results for the Smart Farming Goals.

Irrigation System is the first essential part of the greenhouse.

Picture- 9 Irrigation System Stage Plan.

As we can see there are five timer events, (Ground Humidity, Ground Temperature, Water Conditions, Air

Humidity and Air Temperature) that represent the communication with Smart Sensors. There are Smart

Sensors that measure soil moisture, soil temperature, soil water conditions, ambient humidity and ambient

temperature. All these measurements update the Greenhouse database and the last timer event,

(Greenhouse Status) gather all the latest essential measurements. There are five process tasks, the “Check

Ground Humidity”, “Check Ground Temperature”, “Update Water Conditions”, “Check Air Humidity” and

“Check Air Temperature”. All these process task has entries criteria that evaluate the integrity of the row

data that the smart sensors return and exit criteria that decide when the measurements are completed and

must proceed to update the greenhouse database. We also have the Decision task, “Analyze Greenhouse

status”, that ensure that it gathers all the latest greenhouse measurements and after that it is process the

data and decide if we have reached the milestone must prompt Water Pans or if have reached the milestone

must Open Rooftop or if have reached the milestone must Close Rooftop or if have reached the milestone

Normal Conditions. The discretionary task “Enable Extra Services” is a smart management system that make

suggestions of when to water the plants and of the quantity of the water. The last but not least discretionary

task is the “Emergent Critical Event Expert Handling” that it is only run if a weather condition emergency

take place. In this case a Knowledge Worker must gather all the information about the emergency and will

create a Critical Emergency Report.

Watering Pans is the second stage that we describe shortly, because is essential to keep the greenhouse

plans healthy.

Page: 38

Picture- 10 Watering Plants Stage Plan.

In this stage there are three main human tasks and a discretionary task. The first human task is the “Check

Watering Alarm”. In this task a knowledge worker evaluates the alarms criteria and after complete his work

the second task begins that another knowledge worker identifies the cause that the plants meet the criteria

to be watered and the final decision is being made by the knowledge expert in the third human task. If

knowledge expert decide that the plants must be watered the fourth discretionary task is enabled and the

plants are being watered. In the other case the knowledge expert by pass the alarm and fills the appropriate

report.

The Rooftop Management System is the third very important stage, because it is responsible for the

adaptation of environmental variables always in relation to the conditions resulting from the measurements

of smart sensors, whose processing shows us the current status of our greenhouse, always taking into

account current weather conditions.

Picture- 11 Rooftop Management.

In Rooftop Management stage there is only one mandatory human task the “Final Action Approval” and four

discretionary process tasks the “Alert 2 Open Roof”, the “Alert 2 Close Roof”, the “Open Roof” and finally

the “Close Roof”. The human task “Final Action Approval” is depending on the experience of the knowledge

worker expert to make the final decision to open or to close rooftop in order to maintain and secure the

safety of the greenhouse. The discretionary process task “Alert 2 Open Roof”, is the task that give all the

required information to the knowledge worker expert to open the roof top. The discretionary process task

Page: 39

“Alert 2 Close Roof”, is the task that give all the required information to the knowledge worker expert to

close the roof top. The discretionary process task “Open Roof”, is the task that opens the roof top. Finally,

the discretionary process task “Close Roof”, is the task that closes the roof top.

Air Purification System is the Fourth and last discretionary stage in our Case Plan.

Picture- 12 Air Purification System.

This stage can be activated automatically when the rooftop is closed or opened and occur the execution of

“Enable Air Purification” or the “Disable Air Purification”. “Enable Air Purification” enables air purification

system and gather all essential information of the sensed data in the greenhouse can enable more services

offered by the smart management system, such as suggestions of when to water the plants and of the

quantity of the water. “Disable Air Purification” Disables air purification system and gather all essential

information of the sensed data in the greenhouse can enable more services offered by the smart

management system, such as suggestions of when to water the plants and of the quantity of the water.

Finally, it is good to mention the critical milestones that triggers most important processes in our case.

Picture- 13 Critical Milestones.

Prompt Watering milestone reached as a result of the decision process “Analyze Greenhouse Status” or from

discretionary process task “Extra Services” it is trigger the human process “Check Watering Alarm” of the

Watering Plants Stage.

Normal Conditions milestone reached when all the conditions of the greenhouse are normal. It comes as a

result of the decision process “Analyze Greenhouse Status” and completes an operational circle of the

greenhouse.

Prompt Open Rooftop milestone reached as a result of the decision process “Analyze Greenhouse Status”

and triggers the discretionary process task “Alert 2 Open Rooftop” of the Rooftop Management Stage.

Prompt Close Rooftop milestone reached as a result of the decision process “Analyze Greenhouse Status”

and triggers the discretionary process task “Alert 2 Close Rooftop” of the Rooftop Management Stage.

Page: 40

Critical Emergency Report milestone comes as a result of the discretionary human task “Emergent Critical

Event Handling Expert” and it triggers the human task “Final Action Approval” of the Rooftop Management

Stage.

Open Roof milestone reached after the execution of discretionary process task “Open Roof” of the Rooftop

Management Stage.

Close Roof milestone reached after the execution of discretionary process task “Close Roof” of the Rooftop

Management Stage.

Finally, Extra Services milestone reached after the execution of discretionary process task Enable Air

Purification or ether after the execution of discretionary process task Disable Air Purification. Both tasks are

included to the Discretionary Stage Air Purification.

At the following table we recognize the main characteristics of KiPs to our model and we give an example

for every characteristic.

Page: 41

5.4 Characteristics Meet Requirements

Characteristic Meet Requirements CMMN Example

C1 Knowledge-Driven R1 Data modelling R2 Late Data Modelling R3 Access to appropriate data R4 Synchronized access to shared data R5 Represent data-driven actions R11 Support for different modelling styles R12 Visibility of the process knowledge R16 Learning from event logs R17 Learning from data sources R18 Knowledge workers’ modelling R20 Define knowledge workers’ privileges R21 Late knowledge workers’ modelling R22 Late privilege modelling R23 Capture knowledge workers’ decision

Final Action Approval is a Knowledge-Driven process because it is depending on the experience of the Knowledge Worker that is essential to make the final decision.

C2 Collaboration-Oriented

R1 Data modelling R2 Late Data modelling R3 Access to appropriate data R4 Synchronized access to shared data R12 Visibility of the process knowledge R15 Migration of process instances R18 Knowledge workers’ modelling R19 Formalize interaction between knowledge workers R20 Define knowledge workers’ privileges R21 Late knowledge workers’ modelling R22 Late privilege modelling R23 Capture knowledge workers’ decision

Watering Plants is a Collaboration-Oriented process because is executed in a collaborative multiuser environment, where human-centered and process related knowledge is created by collaboration, shared and transferred by and among process participants with different roles.

C3 Unpredictable R4 Synchronized access to shared data R5 Represent data-driven actions R6 Late actions modelling R8 Late constraints formalization R10 Late Goal Modelling R13 Flexible process execution R14 Deal with unanticipated executions

Emergent Critical Event Handling is an Unpredictable process because a critical condition depends on situation and context specific elements that may not be known a priori, may change during process execution, and may vary over different process cases. The knowledge worker is often not able to predetermine the overall process structure in terms of the

Page: 42

Characteristic Meet Requirements CMMN Example

activities to be executed and their ordering, the data and knowledge sources to be exploited and the roles and resources required for process progression and completion.

C4 Emergent R2 Late Data Modelling R6 Late actions modelling R8 Late constraints formalization R10 Late Goal Modelling R13 Flexible process execution R14 Deal with unanticipated executions R15 Migration of process instances R16 Learning from event logs R17 Learning from data sources R21 Late knowledge workers’ modelling R22 Late privilege modelling R23 Capture knowledge workers’ decision R25 External events late modelling

Emergent Critical Event Handling is also an Emergent process because the actual course of actions gradually emerges during process execution and is determined step by step, when more information is available. Process participants continuously assess process progression and then act or plan the actions to be performed, depending on the process status and the available data and knowledge elements Each performed action and taken decision towards the achievement of a given goal has the effect of producing knowledge. It will be exploited for supporting subsequent decisions and determining the next goals to be achieved as well as the actions to execute.

C5 Goal-oriented R9 Goals Modelling R10 Late Goal Modelling R11 Support for different modelling styles R12 Visibility of the process knowledge

Air purification is a Goal-Oriented process because the process evolves through a series of intermediate goals or milestones to be achieved (Closed Roof, Opened Roof).

Page: 43

Characteristic Meet Requirements CMMN Example

C6 Event-Driven R11 Support for different modelling styles R14 Deal with unanticipated executions R15 Migration of process instances R24 Capture and model external events R25 External events late modelling

Check Ground Humidity, Check Ground Temperature, Update Weather Conditions, Check Air Humidity, Check Air Temperature are typical Event-Driven processes examples.

C7 Constraint and Rule-driven

R1 Data modelling R2 Late Data modelling R3 Access to appropriate data R5 Represent data-driven actions R7 Formalize rules and constraints R8 Late constraints formalization R11 Support for different modelling styles R12 Visibility of the process knowledge R23 Capture knowledge workers’ decision

Analyze Greenhouse Status, is a typical example of a Constrain and Rule-driven example because it has multiple Entry and Exit criteria that drive actions performance and decision making.

C8 Non repeatable R15 Migration of process instances R16 Learning from event logs R17 Learning from data sources

Emergency response plans, for example, are usually unique, as they reflect processes to be applied in a specific emergency situation. The process instance undertaken to deal with a specific case or situation is hardly repeatable, different executions of the process vary from one another. There for Emergent Critical Event Handling Expert is an excellent example of Non repeatable process.

Table - 5 CMMN Elements that required to meet the requirements

At the following pages we will present the most popular CMMN tools that already exists.

Page: 44

6. CMMN Tools

Model of Business is assumed to be considered as an Enterprise Architecture (EA) modelling framework

covering general guidelines of all other business models analysis and applications for the whole enterprise

system architecture design. The classified business models are selectively applied for Enterprise

Architecture (EA) development, therefore modelling languages and notations as well as software tools are

applied upon the system architect requests. It should be noticed that currently available business analysis

software tools support such integrative approaches (M. Pankowska, 2019). In the following section we

present the top CMMN tools.

After a thorough research on the internet community of OMG, we realize that the following CMMN tools

are the most popular and the most representative CMMN tools so far.

6.1 Flowable

Picture- 14 Flowable Technology (Flowable, n.d.)

Flowable is a family of products that use the same technology in order to provide a consistent and appropriate platform for digital operations. Flowable has a range of flexible BPM, CMMN and Case Management products that can meet the different demands of today’s digitization needs. Any defining and customizing processes and cases can be applied across all products. Flowable Work builds on the foundations of Flowable Orchestrate by adding a rich framework for modelling business entities around processes and cases. Powerful document management, integrations, analytics and fine-grain security policies ensure you have a platform to rapidly deliver complete solutions.

✓ Full access to Flowable Orchestrate functionality ✓ Rapid user interface configuration ✓ Business monitoring ✓ Business reporting ✓ Rich model editing environment ✓ Unrivaled test and debug capabilities

Flowable Work

Flowable Work is a flexible technology for delivering dynamic and adaptive process and case management

solutions. Many of the business problems faced by organizations these days can be addressed by solutions

based on Case Management concepts. A case is a way of organizing a set of services, information and

interactions, usually focused on a person or entity. Flowable Work offer an ideal way to manage customer

Page: 45

or client engagements, whether onboarding a new client, handling an insurance claim, dealing with litigation,

or managing a citizen’s social needs.

Picture- 15 Flowable Work (Flowable, n.d.)

With Flowable Work can model your business application, through processes, decision rules, case

structures and forms. Visual editing tools make it easy for the business modeler – expert or citizen

developer – to create rich and sophisticated business models. These models can then be executed

dynamically by Flowable Work to deliver an adaptable business application. Easy to use debug and test

capabilities makes working with complex models a breeze. Built-in Content Management allows media and

documents to be an integral part of cases and processes. Coupled with analytics reports, this enables a

solution to be continuously improved, being as agile as the circumstances require.

Flowable Work is built on the trusted and highly scalable open source Flowable engines, which provide the

power for driving models defined with the open standards BPMN, CMMN and DMN. Everything that is done

in Flowable Work is fully audited, with easy understanding views of the history of a case or process so far.

Page: 46

Flowable Orchestrate

Picture- 16 Flowable Orchestrate (Flowable, n.d.)

Flowable Orchestrate, is a set of business process engines that are compact and highly efficient. They provide the heart of our workflow and Business Process Management (BPM) platform for developers, system admins and business users. Popular with OEMs.

✓ BPMN 2.0, CMMN 1.1 and DMN 1.2 ✓ Embed in your own applications and services ✓ Rich REST and Java APIs ✓ Open Source

At its heart is a lightning fast, tried and tested dynamic BPMN process engine, with accompanying DMN decision tables and CMMN case management engines, all written in Java. They are Apache 2.0 licensed open source, with a committed community. All the engines can run embedded in a Java application, or as a service on a server, a cluster, and in the cloud. They can run as independent engines or services, or seamlessly integrate together to provide a rich suite for business process management. They operate perfectly with Spring. With rich Java and REST APIs, they are the ideal base for orchestrating human or robotic automation processing. They enable microservice orchestration or soft-wire the microservices themselves.

Page: 47

Flowable Engage

Flowable Engage is a solution to support and augment client conversations.

Picture- 17 Flowable Engage (Flowable, n.d.)

Clients can express their ideas, can get personal and knowledgeable responses.

Flowable Engage allows to chat with clients on WeChat, WhatsApp and other popular chat applications, as

well as custom mobile apps, all the time guaranteeing:

✓ Competence, diligence and consistency.

✓ Process to be followed when it needs to be.

✓ Flexibility in handling their needs.

Communication still needs the personal touch and chat apps are becoming the preferred way for businesses

and clients to talk to each other. Flowable Engage is built on the trusted and highly scalable Flowable engines,

which provide the power for driving chatbot models defined with the open standards BPMN, CMMN and

DMN. Flowable Engage provides a Client Conversation Center that:

✓ Prompts for follow ups.

✓ Requests & reminders.

✓ New marketing opportunities.

✓ Conversation as a Case.

✓ 360-degree view of clients.

✓ Digital desk for Relationship Managers.

✓ Respond and chat live with clients.

✓ Add experts to a chat with a click.

✓ Overview of active and recent conversations.

✓ Drill down for chat history.

Page: 48

Flowable is a complete suit and addressed to Modelers, Developers, Administrators and Users.

Picture- 18 Flowable Powerful Suit Technology (Flowable, n.d.)

CMMN – Case Management open standard enables a new world of business automation

Strategically, this one is huge. Introducing a CMMN engine to the toolkit adds a whole new set of dimensions

for modelling intelligent business automation. Most significantly, this engine is a completely native

implementation of the CMMN semantics, so it’s not piggybacking off the BPMN engine. Where there’s

commonality with BPMN execution, such as creating tasks for users, then there are shared services between

the engines to provide that. Combining CMMN, BPMN and DMN as part of a single solution is becoming the

norm. In real-world implementations, we’ve found CMMN to provide a powerful way to model problems

that are very human or event driven. Also, modelers solving complex automations have found it a

sophisticated way of describing the overall end-to-end business activity, managing the different processes

and their relevance in addressing the need at any point in time (Flowable, n.d.).

Page: 49

Picture- 19 Flowable CMMN orchestrate BPMS and DMN (Flowable, n.d.)

Event Stream Integration for the latest architectures

Many solutions these days are using events as the backbone for the interaction between microservices,

systems and people, be it Kafka/Confluent, RabbitMQ, AWS SQS or ActiveMQ/JMS. The ready-to-go

integration in Flowable is both highly scalable and extensible. It’s possible to abstract away from the

underlying event implementation and just work with business events that contain process and case

variables. The low-level implementation can change without affecting the case or process models describing

their effects. There’s even an internal event mechanism now, which provides the benefits of event-driven

automation without actually needing an external framework. Incredibly useful for event-orchestration using

BPMN, it’s even more powerful when coupled with CMMN to provide contextual sensitivity to event-driven

behavior (Flowable, n.d.).

Native BPMN Execution heralds the start of the next generation

This major change was started while the team was working on Activiti but it wasn’t completed until after the

fork to Flowable. There’s a history of the different generations of Java BPM engines that describes the

evolution of introducing an abstract process virtual machine, which was then optimized out in the current

generation of BPMN execution. This capability opens up many options for executing BPMN that would be

near impossible to do with the previous generation engines, while also allowing performance enhancements

(Flowable, n.d.).

Dynamic Process Injection for intelligent adaptation by human or machine

Powerful way of allowing process fragments to be introduced on-demand into a running instance of a

process – either by a user or automatically, for example AI-driven. You can deploy a process model and start

Page: 50

any number of instances of it, then for each of them the model steps will be followed as defined in the

common model. With dynamic injection, individual tasks or even complete process models can be inserted

at any point into a running instance, and that one instance will continue as if the model had originally

included the inserted process. This means a simple, basic process can be modeled without having to account

for all possible exceptional situations, then rely on a human or machine learned system to decide to inject

processes to handle different circumstances (Flowable, n.d.).

Vibrant Community and Development keeps the innovation

Flowable has very active the development on the core Flowable engines. The number of contributions

coming from the community is continually growing. There are people around the planet using Flowable for

pet projects or powering global, mission-critical solutions, and many will help answer questions from

newbies and experienced people alike (Flowable, n.d.).

Asynchronous History means you can run fast without forgetting

Maximizing performance and minimizing database size were some of the key drivers for this capability. The

original way of keeping history in the same database, and having to make decisions about the level of audit

history you need against managing database size, was clearly just a first step. Like others, we’ve added

pruning capabilities to remove history as it ages. But by also adding a completely different approach,

Flowable is able to offer faster throughput and pass transactional history to external systems (typically

NoSQL) for warehousing or analytics. More topically, we’ve used it to feed historic data to machine learning

systems that then feedback into process and case execution (Flowable, n.d.).

Abstract Data Sources always ready for the new and unknown

The database used by Flowable doesn’t need to be relational. As long as the data source can support

transactions, Flowable can use it. While most people do use relational databases with Flowable, there’s

some that are looking for it to run on non-relational databases. The world of databases can always change,

so by providing abstract data sources, Flowable will always be ready to exploit advances, such as with

CockroachDB. It also gives people options to use whatever data sources they want in the way they want.

There’s an experimental integration with MongoDB to illustrate the point (Flowable, n.d.).

Optimized Database Schema designed to meet ever growing performance demands

Flowable has looked hard at how data is stored and queried and how it performs at high throughput and

scale. This has resulted in a bunch of improvements in how data is represented for active processes and

jobs. One of the bottlenecks has been the use of history data sources to find information about previous

steps of live instances. Flowable now keeps all the information about active instances separate from history,

so it doesn’t matter how large your history gets, the runtime performance is optimal. Similarly, jobs of

different types previously shared the same source, but now hold their data separately to ensure the fastest

possible querying of job state (Flowable, n.d.).

DMN DRDs for smarter decisions

With Flowable you can link multiple sets of business rules together to form higher level decisions. DMN is

an open standard for describing decision-making through business rules. Decision Requirement Diagrams in

Flowable allow you to model multiple decision tables connected as dependencies. Instead of using a process

to define the aggregation of business rule outcomes, a single DRD can be used (Flowable, n.d.).

Page: 51

True Parallel Gateways

For all the Activiti-based engines, the execution of tasks after a parallel gateway is not actually parallel: all

the flows are serialized. Flowable has been able to fix this thanks to its new architecture and can execute

parallel flows in a truly parallel way. Not only that, it can execute blocking tasks, such as making REST calls,

highly efficiently (minimal threads, for the technically minded). This isn’t so problematic when dealing with

parallel human tasks, but is critically important when working with microservices or events running in

parallel. And of course, transactional coherence is fully maintained (Flowable, n.d.).

Summary

+ With Flowable Work can model your business application, through processes, decision rules, case

structures and forms.

+ Visual editing tools make it easy for the business modeler – expert or citizen developer – to create

rich and sophisticated business models.

+ These models can then be executed dynamically by Flowable Work to deliver an adaptable business

application.

+ Debug and test capabilities makes working with complex models a breeze.

+ Built-in Content Management allows media and documents to be an integral part of cases and

processes.

+ Coupled with analytics reports, this enables a solution to be continuously improved, being as agile

as the circumstances require.

+ Flowable Work is built on the trusted and highly scalable open source Flowable engines, which

provide the power for driving models defined with the open standards BPMN, CMMN and DMN.

+ Everything that is done in Flowable Work is fully audited, with easy understanding views of the history

of a case or process so far.

- It demands a deep knowledge of the product in order to be able to utilize it correctly.

Page: 52

6.2 Visual Paradigm

Suite of design, analysis and management tools that drive your IT project development and digital

transformation. Agile project tools to help develop great software. Full stack of Enterprise Architecture

Tools. Comprehensive DevOps Tool Suite.

Enterprise Architecture

Industry-unique TOGAF ADM lifecycle tool, DoDAF, NAF and MODAF tool. Used by the world's best-known

enterprises.

✓ Process navigation tool that walks you through the ADM phases.

✓ Actionable steps – Perform the ADM activities within our ADM process tool, with guides and clear

examples to follow – No training required.

✓ Wide range of ADM tools: ArchiMate, Capability Analysis tool, ADM deliverable tool (form)

✓ Auto-generated TOGAF deliverable

✓ Visual Paradigm features an easy-to-use CMMN tool that allows you to visualize cases with CMMN

easily and quickly.

Project Management

Achieve better results by managing your software projects with full-fledge of project management tools.

✓ A map of project management lifecycle with hotspots to all planning, execution and control activities.

Perform the activities with our built-in management tools and generate reports in seconds.

✓ 2D configurable process map that organizes your management activities in a neat way. PMBOK and

various project management process templates are available.

✓ Rich set of management tools: PERT, roadmap, implementation plan, spider chart, WBS, Fishbone,

etc.

Picture- 20 (Visual Paradigm, n.d.)

Page: 53

Agile & Scrum Development

Full set of agile backlog and process management tools that makes your agile projects more effective. User

story mapping:

✓ Drag and drop story creation and arrangement.

✓ Story estimation tool – Affinity table.

✓ Sprint backlog management

✓ Scrum Process Canvas: Scrum Process Canvas that helps your team manage entire Scrum Project in

one page including: all the Scrum roles, events and artifacts. Perform the activities right within the

map. Generate Scrum reports in seconds.

Online Diagrams

Zero setup & configuration based on cloud technology for easy diagram creation and team collaboration. (*

Online diagrams are currently not compatible with most of the UML Desktop Modelling Diagrams)

✓ Convenient drag-and-drop diagram editors

✓ 100+ diagram types, covering all kinds of business, technical and general diagrams.

✓ 1000+ diagram templates to help you start quick.

✓ Web-based – Works well in different web browsers, and in any platform

✓ The best Visio alternative both in terms of features and pricing

✓ Embed your diagrams into MS Documents and Presentations for display and quick editing

User Experience Design

Clarify stakeholders' needs with our powerful user experience tools.

✓ Wireframe tool - Create wireframes to visualize screen flow and screen layout

✓ Wireflow tool – Visualize the flow of wireframes as a flowchart.

✓ Wireflow animation - Make your wireflow alive through the animation tool, which makes your

presentation way more effective.

✓ Prototyping tool: demonstrate and confirm your work.

Code & Database Engineering

Bridge the gap between system design and implementation, with our code and database engineering

support.

Code engineering tools:

✓ Code generation and reversal (for 10+ languages)

✓ Form sequence diagram from Java

✓ Hibernate ORM

✓ Generate/Reverse state machine code

✓ REST API design and generation

Database engineering tools:

✓ Export database/DDL from ERD

✓ Generate ERD from database/DDL

Page: 54

Visual Modelling

Powerful visual modelling tools that help you build and manage your diagrams and model elements:

✓ Drag-and-drop diagram editor

✓ Support UML, BPMN, ArchiMate, DFD, ERD, SoaML, SysML, CMMN.

✓ Effective modelling tools such as elements reusability, diagram & element transformation, syntax

validation, custom properties, etc.

✓ Many formatting options.

Virtual Paradigm CMMN tool:

✓ Support link with BPMS (we can have process task in CMMN diagram linked to BPMN processes).

✓ Export CMMN diagrams as images (PNG, PDF, JPG and SVG).

✓ Explain case model with annotation (We can add an annotation layer and describe the events, stages,

milestones in detail).

Teamwork

Provide teamwork with a cloud-based repository where members and projects are organized and accessible.

Team members simply open a project from the repository, start working in Visual Paradigm, and then

commit the changes back to the repository, and that's it. Changes are then accessible to all the other team

members.

Summary

+ Visual Paradigm is a professional suite of design, analysis and management tools that drive the IT project

development and digital transformation.

+ It has a huge variety of project management tools and it is a complete suite that can cover all needs of

the Business Project Management.

+ Provide Teamwork with a cloud-based repository where members and projects are organized and

accessible.

+ CMMN Diagram Tool provide:

✓ Link CMMN with BPMN and DMN.

✓ Export CMMN diagrams as images in PNG, PDF, JPG and SVG formats.

- Visual Paradigm does not have an execution CMMN model engine yet.

Page: 55

6.3 Trisotech

Picture- 21, (Trisotech Digital Modeling Suite, n.d.)

Digital Modelling Suit

Digital Modelling Suit, create a digital twin of your organization using visual models.

The Digital Modelling Suite is a simple browser-based suite of visual applications that are intuitive enough

for business users, yet sufficiently powerful to delight IT users. It brings together creative thinking and

critical thinking, using recognized methods to guide and capture brainstorming sessions and structured

operational models. It allows organizations to align operations to strategic intent all the way through to

analysis and automation. The Digital Modelling Suite is a complete set of visual modelling applications that

captures the DNA of your organization in a digital twin.

Creating Visio drawings describing your operations is a great first step toward Digital Transformation. To fully

leverage your existing Visio drawings and take them to the next level, you can import them in the Trisotech’s

Digital Enterprise Suite to instantly obtain these benefits:

✓ Turn your diagrams into flexible and reusable models that can be versioned.

✓ Check correctness and validate your models against the standard specification.

✓ Export your models using the standard interchange file format.

✓ Augment your models with documentation and multimedia content and then animate them.

✓ Collaborate and share your work with the rest of your organization via modern Web Browsers.

Discovery Accelerator

The Discovery Accelerator is a web-based application for non-technical business users, used to organize

together business goals, activities, systems, stakeholders and events. Users can start from existing

documentation, from brainstorming sessions, or from unearthing user stories. This simple drag and drop

application accelerate the discovery of key business entities, the analysis of their interrelationships, and

visually exposes any gaps. The models created with the Discovery Accelerator can be exported in various

formats for documentation or further business modelling.

Workflow Modeler

The Workflow Modeler is a web-based application used to document business processes and to drive process

improvement initiatives. This application is used by both business and IT personnel to develop workflows as

interactive documentation of existing and desired processes. Users can create complete workflow models

that can be easily published as web services in one click. The Workflow Modeler creates models as per the

Page: 56

Business Process Model and Notation (BPMN 2.0) standard, ensuring optimal interoperability with other

compliant software applications.

Capability Modeler

The Capability Modeler is a cloud-based strategic planning and business architecture application for

capturing and defining the basic building blocks of your organization. Map business capabilities and their

relationships to operations in an easy-to-use environment designed for business users. Align operations to

strategic intent using this high-level common definition of what the business does.

Case Modeler

The Case Modeler is a web-based application used to document case-oriented business processes and to

drive process improvement efforts for knowledge work. This application is used by both business and IT

personnel to develop case models as interactive documentation of existing and desired case-oriented

processes. Users can create complete case models that can be easily published as web services in one click.

The Case Modeler creates models as per the Case Management Model and Notation (CMMN) standard,

ensuring optimal interoperability with other compliant software applications.

Decision Modeler

The Decision Modeler is a web-based application used to build business decisions that are both explainable

and traceable. Using a simple graphical user interface, business and IT users can collaborate to specify both

the requirements and the logic of decisions. These fully specified decisions can then be directly automated.

The Decision Modeler is based on the Decision Model and Notation (DMN) open standard, ensuring optimal

interoperability and compliant execution.

Digital Enterprise Graph

The Digital Enterprise Graph enables global reuse of modelling elements across your organization. It is a

digital twin of your organization automatically generated from all your existing models. This semantic digital

twin provides insight into all modelling elements created across all your models and the various relationships

that exist amongst them. Business vocabularies (terms and definitions) that are created are also included in

the graph and can be reused. The Digital Enterprise Graph fuels reasoning capabilities of the who, what,

when, where, why and how captured in your models. The relationships between modelling elements in the

graph can be detected, explored, and leveraged, while maintaining traceability between the reused

modelling elements and the original models. The Digital Enterprise Graph can not only index models created

using the Digital Modelling Suite, it can also index relationships to select third-party tools.

Knowledge Entity Modeler

The Knowledge Entity Modeler is a simple to use, meaning-centric web application that ensures clear and

unambiguous communications across an organization. Business and IT users can jointly specify business

vocabularies containing the terms they commonly use along with their exact meanings. Concept maps,

logical information structures, and business rules can also be specified providing a clear expression of

meaning to the organization. The resulting semantically rich common business language is reusable in

narrative communications, business process, case and decision models, as well as in software applications

specifications.

Page: 57

Landscaping

Landscaping is a cloud-based strategic planning and lightweight business architecture tool. Creative strategic

thinking and unconstrained brainstorming is supported with electronic sticky notes to capture quality ideas

in an intuitive and easy-to-use environment. Create business architecture blueprints to bridge the gap

between strategic brainstorming and the operations of your organization.

Summary

+ Can define extra setting to a task as lag time process time, cost setting, value, quality and more in order

analyzing work efforts.

+ The teamwork ribbon allows you to work with the collaborative features in the Case Modeler.

+ You can get access to manage your repository options.

+ You can and add and view posted comments made by others and yourself.

+ You can also access the versioning capabilities to define the state and version of your models and share

your model with others via the share function.

Page: 58

6.4 Signavio

Picture- 22 Signavio Business Transformation Suit (Signavio, n.d.)

Signavio Business Transformation Suite, is a source for a web-based, collaborative, integrated set of tools

that transform ideas into actions. Signavio’s intuitive and modern Business Transformation Suite, can

deliver effective change faster through its integrated platform.

✓ Intuitive process modelling.

✓ Innovative decision modelling.

✓ Collaborate with the majority of colleagues.

✓ No software installation required.

✓ High data security protection.

Business processes are the foundation on which your organization is built. The Signavio Business

Transformation Suite offers you an integrated solution that allows you to mine, model, analyze, optimize,

and execute your processes and all at the accelerated speed of insights.

SAP Process Intelligence by Signavio

Delivers next-generation process mining at scale, providing powerful fact-based insights into potential risks

and ongoing improvement opportunities for smarter business decisions. The powerful combination of

process discovery, process analysis, and conformance checking supports a collaborative approach to

process improvement, giving you game-changing insights into your business. Run in-depth process analysis

for actionable improvement, while evaluating change alternatives & implementing best practices. Establish

smart diagnostics to test weaknesses & simulations to optimize processes for RPA & ERP transformations.

SAP Process Collaboration Hub by Signavio

SAP Process Collaboration Hub by Signavio is the collaborative capability to implement business

transformation across the entire Suite, underpinning excellence strategies for organizations across

industries and geographies. The Hub is a step-up in operational reinvention, and streamlines coordination

across stakeholders during process mining, process design, and process execution. The Hub helps you

transform business and workspaces, with modern commenting functions, automatic notifications,

presentation mode, and more. With the accelerated wisdom of the crowd, companies generate more

ideas, optimize processes, and reduce resistance to digital change.

SAP Process Manager by Signavio

Page: 59

SAP Process Manager by Signavio is an intuitive BPM solution for professional process modelling. Whether

you want to create current-state documentation or target concepts, SAP Process Manager by Signavio is

your best choice. Thanks to innovative web technology, you can get started right away and include your

colleagues in collaborative design across modelling, analyzing, optimizing, and executing processes within

your organization. Put your business on the path to operational excellence with a refreshingly modern and

easy-to-use interface. Use the share functionality to instantly share modelling feedback, reviews, and

comments across the Suite. In Signavio Process Manager, you can model the standard framework of the

corresponding process in a BPMN diagram and then link a BPMN sub-process to a CMMN diagram that

defines flexible sequences. You can also change a task in a BPMN diagram to a sub-process that links to a

CMMN model, to define a number of flexible actions more accurately. You can seamlessly integrate CMMN

into BPMN and DMN-diagrams, to complement your existing process landscape. CMMN allows you to

more accurately model highly variable processes, such as working with patient files or managing customer

support processes.

SAP Workflow Accelerator by Signavio

Turn your business process models into standardized workflows that can be rolled out across your entire

organization. Go beyond task management and ticket systems to capture, connect, and communicate how

work is done. Coordinate tasks, keep track of work and ensure rapid accountability & collaborative process

governance. Connect workflows to process mining investigations! The result is a high level of consistency

and efficiency throughout your company, supported by an easy-to-use and accessible workflow engine to

drive success, intelligence, business transformation, and customer understanding.

Summary

+ Intuitive process modelling.

+ Innovative decision modelling.

+ Collaborate with the majority of colleagues.

+ No software installation required.

+ High data security protection.

- CMMN only available to business licenses.

Page: 60

6.5 Camunda

Picture- 23 Camunda Modeler Design Editor (DEEHAN, 2020)

Realizing the potential of an executable case management solution, Camunda’s very own Falko

Menge joined the OMG’s CMMN taskforce and, along with its other members, helped shape the standard.

They moved quickly to embrace CMMN. As a company famous for their Java BPMN engine, they decided to

start with a CMMN engine. They assumed, as with what happened with BPMN that other vendors were

building CMMN modelers. Knowing that they would all be standard compliant, a user could model a CMMN

diagram in a modelling tool and then execute that model with the Camunda Engine. In the same year that

the standard was released, they released their engine. Reading this blog post from 2014, they get a good

impression of how fast they were moving.

The movement was not just about technical implementation — the consulting team was working on learning,

creating, and delivering training for people interested in using CMMN.

Earlier in the year, they had started to build bpmn.js, the underlying project now ubiquitous across many

BPMN and workflow projects. Having already seen success with it we started to build cmmn.js, so that as

well as having an engine implementation they would have a visual representation with the goal of a fully

functional modeler, something they would end up achieving as early as version 1.1 of the Camunda

Modeler.

Page: 61

Picture- 24 Camunda Modeler (DEEHAN, 2020)

The final part of the puzzle came with tooling for use in runtime. For this, they added a CMMN viewer in

their admin tool cockpit so that administrators could see what was going on and what had already happened

for the cases deployed.

They not only wanted CMMN to be a success – they actively fought for it. They made CMMN the topic of

keynote presentations at various conferences and eager to find use cases they also educated all our

customers and prospects about it (DEEHAN, 2020).

Summary

After just a few short years of the CMMN specification being published Camunda had:

+ Developed an open source CMMN engine

+ Developed a CMMN modeler and viewer

+ Created and delivered expert training

+ Developed admin tools to make CMMN ‘in production’ more achievable

+ Even before the Object Management Group (OMG) had released the CMMN Specification in 2014,

Camunda had already started building a CMMN engine. In the years that followed, Camunda invested

in symbol support, modelling capabilities, and admin tools.

- But after a couple of years, Camunda decided that we would not be adding any more CMMN features

to Camunda and that it will simply be maintained but not fully supported.

Page: 62

6.6 BPMN.io

Picture- 25 BPMN.io Modeler Design Editor, (BPMN.IO, n.d.)

A completely free Web-based tooling for BPMN, DMN, and CMMN. CMMN Viewer and Editor visualize case

plans modes, cmmn-js simplifies creating, embedding and extending CMMN 1.1 diagrams.

+ It runs in modern browsers.

+ It can be used standalone or integrate it into applications and it can also add CMMN diagrams into

application (Embed CMMN diagrams into application with a few lines of code). The tools are built and

tested by Camunda. The libraries are extensible, embeddable and open source on GitHub.

- Does not support collaboration platform.

- Does not support execution engine.

It is worth to mention that with this tool we designed our case study, because it is free and it is available on

web without the need of any installation. CMMN Viewer and Editor is very easy to use it, it has the

majority of CMMN elements and it is recommended for anyone who wants to design a CMMN diagram

nice and easy. BPMN.io tools are built and battle tested by Camunda and an integral part of the Camunda

product stack.

Page: 63

6.7 CMMN Tools Overview

Flowable Visual Paradigm Trisotech Signavio Camunda BPMN.io ₊ Flowable Work can model

your business application, through processes, decision rules, case structures and forms.

₊ Visual editing tools make it easy for the business modeler expert or developer to create advanced business models.

₊ These models can then be executed dynamically by Flowable Work to deliver an adaptable business application.

₊ Debug and test capabilities makes working with complex models a breeze.

₊ Built-in Content Management allows media and documents to be an integral part of cases and processes.

₊ Coupled with analytics reports, this enables a solution to be continuously improved, being as agile as the circumstances require.

₊ Flowable Work is built on the trusted and highly scalable open source Flowable engines, which provide the power for driving models defined with the open standards BPMN, CMMN and DMN.

₊ Everything that is done in Flowable Work is fully audited, with easy understanding views of the history of a case or process so far.

₋ It demands a deep knowledge of the product in order to be able to utilize it correctly.

₊ Visual Paradigm is a professional suite of design, analysis and management tools that drive the IT project development and digital transformation.

₊ It has a huge variety of project management tools and it is a complete suite that can cover all needs of the Business Project Management.

₊ Turn your diagrams into flexible and reusable models that can be versioned.

₊ Check correctness and validate your models against the standard specification.

₊ Export your models using the standard interchange file format.

₊ Augment your models with documentation and multimedia content and then animate them.

₊ Collaborate and share your work with the rest of your organization via modern Web Browsers.

₊ CMMN Diagram Tool provides link CMMN with BPMN and DMN.

₋ Visual Paradigm does not have an execution CMMN model engine yet.

₊ Turn your diagrams into flexible and reusable models that can be versioned.

₊ Check correctness and validate your models against the standard specification.

₊ Export your models using the standard interchange file format.

₊ Augment your models with documentation and multimedia content and then animate them.

₊ Collaborate and share your work with the rest of your organization via modern Web Browsers.

₊ Can define extra setting to a task as lag time process time, cost setting, value, quality and more in order analyzing work efforts.

₊ The teamwork ribbon allows you to work with the collaborative features in the Case Modeler.

₊ You can get access to manage your repository options.

₊ You can and add and view posted comments made by others and yourself.

₊ You can also access the versioning capabilities to define the state and version of your models and share your model with others via the share function.

₊ Models defined with the open standards BPMN, CMMN and DMN

₊ Empowered by SAP. ₊ Intuitive process modelling. ₊ Innovative decision modelling. ₊ Collaborate with the majority

of colleagues. ₊ No software installation

required. ₊ High data security protection. ₊ CMMN Diagram Tool provides

link CMMN with BPMN and DMN.

- CMMN only available to business licenses.

+ Developed an open source CMMN engine

+ Developed a CMMN modeler and viewer

+ Created and delivered expert training

+ Developed admin tools to make CMMN ‘in production’ more achievable

+ Even before the Object Management Group (OMG) had released the CMMN Specification in 2014, Camunda had already started building a CMMN engine. In the years that followed, Camunda invested in symbol support, modelling capabilities, and admin tools.

₋ But after a couple of years, Camunda decided that we would not be adding any more CMMN features to Camunda and that it will simply be maintained but not fully supported.

+ It runs in modern browsers. + It can be used standalone or

integrate it into applications and it can also add CMMN diagrams into application (Embed CMMN diagrams into application with a few lines of code).

+ The tools are built and tested by Camunda. The libraries are extensible, embeddable and open source on GitHub.

₋ Does not support collaboration platform.

₋ Does not support execution engine.

Page: 64

7. CMMN tool evaluation for KiPs In this session we will present the top three CMMN tools (Flowable, Visual Paradigm and Trisotech), we will

describe the differences and we will make an evaluation of them, during the modelling and execution stage

examining how these tools are satisfy the KiPs requirements.

7.1 Flowable Presentation We created a new project from scratch to demonstrate who this business tool work.

Picture- 26 Flowable design environment.

Flowable design environment sow us from the very beginning that has a greate variety of tool and new features. It is

a professional application that give the opportunity not only to design your model, but to execute it as well.

Page: 65

At the following table we present the basic elements of Flowable toolbox.

Containers

Stages are used to structure cases. They are often used to represent the different phases of a case or to logically group plan items. It is possible to nest stages.

Controls

A human task is used to model work that needs to be done by a human actor. When the task is activated, a new task is created in the task list of users or groups assigned to the task. It is possible to provide a form reference which will be displayed when the user opens the task.

A case task creates a new task once it is activated. It will complete as soon as the case is completed unless ‘blocking’ is set to false.

A process task starts a new process once it is activated. By default, the task will be completed once the process completes. To overwrite this behavior, deactivate the ‘blocking’ attribute.

A case page is used to model work that needs to be done by a human actor. The main difference with a human task is that no task is created for a case page task, only a plan item instance.

Milestones represent an achievable target. They do not have execution semantics but can be used to control the flow and improve the clarity of models.

Flowable Work

A service task for initializing variables in the process or case context. Use expressions to define the target work to set a variable for, or to set the variable value.

Generate a document based on a word template.

Create a document based on form byte array, base64 string or input steam.

Merge multiple PDF documents into one PDF document.

A service task using a service definition defined in the service registry engine.

Create audit log.

Chat Activities

Create or re-uses a new conversation with lots of options for the conversation setup.

Allows you to modify an existed conversation by adding or removing participants, setting its name or description or adding tags.

Creates and sends a new message in a conversation with support of privet, sticky and even action messages.

Service Tasks

Tasks are plan items that are completed as soon as they are activated. Use them to model manual work or on other occasions where no runtime.

A service task is a task that uses some kind of service, such a custom code, a web service or an automated application.

A script task is executed by a script engine, such as Java script or Groovy. The script must be defined in a language that the engine can interpret. When the task is ready to start, the engine will execute the script. When the script is completed, the task will also be completed.

Page: 66

Decision tasks call a DMN table and store the results in one or more variables defined in the table.

A task used sent out an e-mail.

A task that allows to submit and store the result of an HTTP call.

A task used sent out an event.

A task that creates a job which can be executed by an external worker.

Sentries

Entry criterion are used to control when a plan item will be activated / enabled. Once the conditions evaluate to true, the plan item they are attached to will be activated / enabled. Plan items can connect to entries criteria to make their evaluation depend on another plan’s items state transition.

Exit criterion are used to control when a plan item will be terminated / exited. Once the conditions evaluate to true, the plan item they are attached to will be terminated / exited. Plan items can connect to exit criteria to make their evaluation depend on another plan’s items state transition.

Listeners

Timer event listener allows to set a timer whose expiration triggers the evaluation of the connected sentries.

User event listener allows to manually trigger the evaluation of the connected sentries, through the user interface.

A signal event listener allows to listen from signal events, also from a BPMS process instances.

A user event listener allows to trigger the evaluation of connected sentries in the backend using the ‘triggerPlanItemInstance’ method of the CmmnRuntimeService.

Connectors

Connects plan items with entry and exit criteria thus representing the ‘on-part’ of the sentries.

Table - 6 Flowable CMMN Elements

You can drag and drop the CMMN elements to your case and with click on tool symbols you can set the

CMMN object parameters.

Flowable has a great collection of CMMN tools that gives flexibility to design in more detail your model.

The first thing we notices is that Flowable does not have a case file item, but it has a case page process

instead of case file item. A case page is used to model work that needs to be done by a human actor. The

main difference with a human task is that no task is created for a case page task, only a plan item instance.

In Flowable for every process task that you design it is mandatory to define as well the BPMN process

reference and key in order to be able to run your project.

At the following picture we see the process setting tab list and a simple example of a BPMS process to

demonstrate the bond between CMMN diagram and a BPMS process diagram.

Page: 67

BPMS Diagram Enable Extra Services

Picture- 27 Flowable Process Task Settings With a simple BPMS Process Example.

The first process examines greenhouse measurements, after there is an exclusive gateway to decide if must

raise the water alarm or not. After that the decision task Enable Other Extra Services decide what other extra

services must be enabled, finally the process Update Greenhouse Status, updates greenhouse database.

In Flowable you can create a User Interface (UI) forms for human tasks to give the design of the form that it

can be very close as the final UI that the UI developer decide to create during implementation. The following

example give a demo look of the Emergency Handling human task.

Picture- 28 Flowable Form Reference of Human Task Emergent Critical Event Handling Expert.

In Flowable you cannot design discretionary tasks, discretionary plan flagments and discretionary stages.

The Smart Farming Case Diagram is different from the diagram that we have firstly designed with the

BPMN.io., because we replaced the case file items with the case page task, all stages and task are mandatory

because of the lack of capability to design discretionary tasks and stages. All plan items mast have sentries

in order to establish a connection between them.

Page: 68

The time that we have spent in order to design our case diagram with the Flowable is a double time because

it has a lot of setting that must be defined manually.

We have also to design the BPMS models for every process task.

We have to create the User Interfaces (UI) forms for the human tasks.

The connections of the CMMN Process with BPMS and DMN is a very powerful feature and gives the

opportunity to design your model thoroughly and in a most precisely way with every detail even the design

and the behavior of the User Interface of the App Model.

The last and most important feature of Flowable suit is the ability to execute your model as well.

It has a team oriented platform that all participants can consecrate, change ideas and while model execution.

Picture- 29 Flowable Engage.

Flowable is a family of products that use the same technology in order to provide a consistent and

appropriate platform for digital operations.

Flowable suit an excellent choice for supporting Object Management Group (OMG) because provide an

integrated environment for BPMS, CMMN and DPMN that all OMG notation can cooperate to provide the

best modelling results.

At the following picture we see the CMMN diagram that we have designed with the Flowable.

Page: 69

Picture- 30 Flowable design Smart Farming Case diagram.

Page: 70

In order to load your Case to Flowable Engage, you must first publish your project by clicking to the command

button “Publish App Model”.

Picture- 31 Flowable App publishing.

The following error occurred to my app model and I was not be able to overcome this problem because the

error description it was very generic and my knowledge to Flowable Tool was not enough to solve it and run

my model.

Picture- 32 Flowable App publishing.

I could not manage to publish my model because it has a lot of things that you must create and a lot of

setting that you must define to your model in order to be able to publish it. It is difficult to use it because it

demands a deep knowledge of this tool.

Page: 71

7.2 Visual Paradigm Presentation

Case Management (CMMN) Diagram Tool

Business scenarios can be highly dynamic in that the workflow involved and the outcomes depend a lot on

ad-hoc events and decisions. The OMG Case Management Model and Notation (CMMN) is a declarative

modelling language that provides an agile and dynamic way to address the situations that may happen within

a context, known as a case. By drawing a CMMN diagram, you can visualize the events that may happen in

the context of a case, the tasks involved, and the milestones. Visual Paradigm is a professional suite of design,

analysis and management tools that drive your IT project development and digital transformation. It has a

huge variety of project management tools and it is a complete suite that can cover all needs of the business

project management. For the scope of our evaluation, we will discuss the CMMN modelling tool that it is

included to this marvelous suit.

CMMN Diagram Tool provide:

✓ Link with BPMN. Have process tasks in your CMMN diagram linked to a BPMN processes.

✓ Explain case model with annotation. Add an annotation layer and describe the events, stages,

milestones in detail.

✓ Export CMMN diagrams as images. Export your CMMN case models as images in PNG, PDF, JPG and

SVG formats.

First, we will create a new CMMN diagram from scratch to demonstrate the visual paradigm environment.

Picture- 33 Visual Paradigm CMMN Design Environment.

Page: 72

At the following table we present the Visual Paradigm CMMN toolbox.

Name Description

Create a case plan model.

Create a case file item.

Create a Plan Flagment.

Create a stage plan Item.

Create all types of Tasks. (Task, Human Task, Process Task, Case Task and Decision Task).

Create a Milestone Plan item.

Create all types of an event listener (Event, Timer Event and User Event).

Create all types of sentries (Entry and Exit Criterion).

Specifies the event on the source case file item that serves as a trigger.

Specifies the event on the source plan item that serves as a trigger.

Defines the scope of planning by identifying a sub-set of plan item definitions that can be considered for planning in the certain context.

Create all discretionary items (Task, Human Task, Case Task, Decision Task, Process Task, Stage and Plan Flagment)

Visualize the dependency between a human task and a discretionary item.

Used to link information and Artifacts with CMMN graphical elements.

A mechanism for a modeler to provide additional text information for the reader of a CMMN diagram.

Table - 7 Visual Paradigm CMMN Elements.

In Visual Paradigm you must chose the wright connects for the CMMN elements connections. For example,

to connect a listener event with a task you must use the “Plan item on part” connector to connect a task

with a case file you must use the “Case file item on part” connector.

You must manually define the label of the connector by double clicking the connector.

In Visual Paradigm you cannot connect two tasks without sentries’ criterions.

The design environment checks always the syntax of the model and prevent the designer from mistakes

during creation of the model.

Visual Paradigm is one of the best teamwork environments it very easy to share your project with other

people and work together without any problem of losing your job or experience any collisions.

The Visual Paradigm diagram has a few differences from the Flowable and BPMN.io diagram because it

demands to have sentries’ criterions in order to establish your associations, the Visual Paradigm toolbox

supports the case file item and Visual Paradigm supports the design of discretionary task, stages and plan

flagments.

Unfortunately, Visual Paradigm does not support an execution engine at the moment, but if you must have

a suite to support the whole package of BPM that is definitely the Visual Paradigm because it is a complete

BPM suit that all models share information and are related to each other.

At the following page you can see the Greenhouse model as it designed by Visual Paradigm CMMN modeler

editor.

Page: 73

Picture- 34 Visual Paradigm design Smart Farming Greenhous Case diagram.

Page: 74

7.3 Trisotech Presentation

Picture- 35, Case Modeler (Trisotech Digital Modeling Suite, n.d.)

The Digital Modelling Suite is a simple browser-based suite of visual applications that are intuitive enough

for business users. It allows organizations to align operations to strategic intent all the way through to

analysis and automation. The Digital Modelling Suite is a complete set of visual modelling applications.

Document Case-Oriented Business Processes, Create CMMN case models for less-repeatable, event-driven,

content-centric business scenarios that depend on ad hoc runtime decisions by knowledge workers Identify

stages, content, mandatory and discretionary tasks, milestones, manual and automated decisions, events,

and their interrelationships Import models from CMMN files. Develop Case Models as Interactive

Documentation Link a process task to a Workflow Modeler workflow Link a decision task to a Decision

Modeler decision Link from a Workflow Modeler case task to a case Create semantic links to industry

frameworks and enterprise architecture tools Add documentation to case elements and models Add

comments for real-time team collaboration. Analyze, Publish and Share Validate case models against the

CMMN standard Animate cases with multimedia attachments to interactively step through the logic Export

executable case models Export case diagrams as documentation Publish case models as web services for

automation (Digital Automation Suite required).

To demonstrate the Trisotech Case Modeler we create a new CMMN model.

Page: 75

Picture- 36 Trisotech Case Modeler Design Environment.

At the following table we present the Trisotech Case Modeler CMMN toolbox.

Name Description

Create all types of Tasks. (Task, Human Task, Process Task, Case Task and Decision Task).

Create all discretionary items (Task, Human Task, Case Task, Decision Task, Process Task and Stage).

Create all types of an event listener (Event, Timer Event and User Event).

Create a Collapsed Stage, Collapsed Plan and discretionary Collapsed Stage.

Create an Expanded Stage, Expanded Plan and discretionary Expanded Stage.

Create a Milestone.

Create a Collapse Plan Fragment.

Create an Expanded Plan Fragment.

Create a Case File Item.

Create an Entry Criterion.

Create an Exit Criterion.

Connectors that represent sentry on parts can be used to visualize (Possible Complex) dependencies between plan items and discretionary items.

Links a Human task with its discretionary items.

Links Information and Artifacts with CMMN graphical Elements.

An object can be associated with an annotation to provide additional documentation.

A Knowledge Source denotes an authority.

Page: 76

Add an image.

Create a case plan model.

Table - 8 Trisotech Case Modeler CMMN Elements.

The design editor is perfect, very easy to use it, the drag and drop works smoothly and with great

precession. With a very few moves you can design every CMMN element.

Export

Picture- 37 Trisotech Case Modeler Import-Export.

The CMMN Modeler allows you to export into the CMMN and Image as well as Word and HTML reports

type file formats. Trisotech Case Modeler Export the CMM diagram as JPEG, PNG, TIFF and PDF.

You can define extra setting to a task as lag time process time, cost setting, value, quality and more.

Picture- 38 Trisotech Case Modeler Extra Task Setting.

The final diagram is slightly different with the first diagram that we have designed with the BPMN.io CMMN

editor because Trisotech Case Modeler demands to have sentries to your task in order to establish an

association between them. Trisotech Case Modeler model is also different form the Flowable diagram

because it supports case file items and the design of discretionary task, stages and plan flagments. Trisotech

Case Modeler model is the same with Visual Paradigm because, Visual Paradigm modeler editor has similar

modelling standards with Trisotech Case Modeler editor.

You can also make reference to BPMS Process but it is not mandatory.

Trisotech provide a very powerful suit and it is for sure a complete application that can cover all the demands

of Object Management Group (OMG).

Page: 77

Picture- 39 Trisotech Case Modeler Design Smart Farming Greenhous Case diagram.

Page: 78

Teamwork

Picture- 40 Trisotech Teamwork.

The teamwork ribbon allows you to work with the collaborative features in the Case Modeler. You can open

and save your files, get access to the manage your repository options and add and view posted comments

made by others and yourself. You can also access the versioning capabilities to define the state and version

of your models and share your model with others via the share function.

Case Animator

Picture- 41 Trisotech Case Animator.

The Case Animator feature allows you to socialize the interaction with the created case. It allows you to see

exactly how and what happens when different parts of the case are interacted with. You can correct you

errors and see if you miss something on design time. Also, you are able to attach files, links and video to your

cases, as well as toggle the block attachments and block models function during the animation process.

Page: 79

Picture- 42 Trisotech Case Animator Example Presentation.

The picture above present as how interacts events and process of Irrigation System Stage.

I could not be able to publish my model because Digital Automation Suite required and I did not have the

way to gain access to it.

Digital Automation Suite

The Digital Automation Suite is an API-first, container-based scalable cloud infrastructure for business

automation. It enables complex automation of business workflows, cases and decisions in a simple,

integrated run-time environment. It allows organizations to leverage business automation as a source of

competitive advantage, with performant, highly flexible, and linearly scalable engines. The Digital

Automation Suite fosters an outcome-driven orchestration of AI and other emerging technologies using

international standards in a micro service architecture. This was not available to me in trial licence.

Picture- 43 Trisotech Digital Automation Suit.

Page: 80

7.4 CMMN Tools Comparative Overview Visual Paradigm

Picture- 44 Visual Paradigm Overview, (Visual Paradigm, n.d.).

Visual Paradigm is a professional integrated suite of design, analysis and management tools that drive the Information Technology project development and digital transformation. It has a huge variety of project management tools and it is a complete suite that can cover all needs of the Business Project Management. Provide an excellent Teamwork with a cloud-based repository where members and projects are organized and accessible. CMMN Tool provide link CMMN with BPMN and DMN. You can export CMMN diagrams as images in PNG, PDF, JPG and SVG formats. The design environment has error correction system that prevent you from making errors on your diagrams. It is not so handy as Flowable and Trisotec during design time. For examples you must be very patient, accurate, and aware during connections of CMMN elements. It has a complete toolbox of CMMN elements and in Visual Paradigm everything you build in your project can be utilized form another relative task and give you the opportunity to create a complete business project with the position of one application only. The Visual Paradigm. The fact that it does not yet have its own CMMN modeling machine is a small thorn in the CMMN evaluation side, but I think it is just a matter of time and in future versions it will incorporate this feature as well. Flowable

Picture- 45 Visual Paradigm Overview, (Flowable, n.d.).

Page: 81

Flowable is an advanced business model design with a very handy design environment. Create models of cases, process and forms with all the advanced editing features that help make consistent. Flowable Design is the solution for working on your own models or collaboratively as part of a business modeling team. Flowable Design has a built-in model repository that allows you to share models within your team or organization. This enables reuse of expertise already captured in business models and helps avoid constant reinvention of business processes. The model repository also maintains previous versions, so it’s always possible to go back to an earlier snapshot of a model. With Flowable Work can model your business application, through processes, decision rules, case structures and forms. Visual editing tools make it easy for the business modeler expert or developer, to create advanced business models. These models can then be executed dynamically by Flowable Work to deliver an adaptable business application. Debug and test capabilities makes working with complex models in more flexible way. Built-in Content Management allows media and documents to be an integral part of cases and processes. Coupled with analytics reports, this enables a solution to be continuously improved, being as agile as the circumstances require. Flowable Work is built on the trusted and highly scalable open source Flowable engines, which provide the power for driving models defined with the open standards BPMN, CMMN and DMN. Everything that is done in Flowable Work is fully audited, with understanding views of the history of a case or process so far. From the CMMN toolbox I still mish the case file item as I understand that flowable has replaced it with case page process. Finally, I do not agree with the lack of discretionary task, stages, plan fragments. It is also demanding to be experienced in notation modeling in order to utilize their features and execute your models correctly, but in overall, I believe that is great business suit for the notation modeling.

Trisotech

Picture- 46, Digital Enterprise Suite (Trisotech Digital Modeling Suite, n.d.).

Trisotech Digital Modelling Suite is a simple browser-based suite of visual applications that are intuitive

enough for business users. It allows organizations to align operations to strategic intent all the way through

to analysis and automation. The Digital Modelling Suite is a complete set of visual modelling applications.

Develop Case Models as Interactive Documentation Link a process task to a Workflow Modeler workflow

Link a decision task to a Decision Modeler decision Link from a Workflow Modeler case task to a case Create

semantic links to industry frameworks and enterprise architecture tools Add documentation to case

elements and models Add comments for real-time team collaboration. Analyze, Publish and Share Validate

case models against the CMMN standard Animate cases with multimedia attachments to interactively step

Page: 82

through the logic Export executable case models Export case diagrams as documentation Publish case

models as web services for automation with the Digital Automation Suite. Trisotech modeler in my opinion

has the handiest design environment and support all CMMN element and provide no restrictions. In addition,

it has less complexity than the Flowable on demanding modeling experience. Finally, it is analyzing work

efforts by allowing to set in the task’s properties the time, cost, value and quality. For me Trisotech is my

preferable choice between Visual Paradigm and Flowable according the CMMN language.

Summarizing

Flowable Visual Paradigm Trisotech + Can model business application,

through processes, decision rules, case structures and forms.

+ Visual editing tools make it easy for the business modeler expert or developer to create advanced business models.

+ These models can then be executed dynamically by Flowable Work to deliver an adaptable business application.

+ Debug and test capabilities makes working with complex models in more flexible way.

+ Built-in Content Management allows media and documents to be an integral part of cases and processes.

+ Flowable Work is built on the trusted and highly scalable open source Flowable engines, which provide the power for driving models defined with the open standards BPMN, CMMN and DMN.

- It demands a deep knowledge of the modeling in order to be able to utilize it correctly.

- It does not support discretionary items.

- It does not have case file item.

+ Visual Paradigm is a professional suite of design, analysis and management tools that drive the IT project development and digital transformation.

+ It has a huge variety of project management tools and it is a complete suite that can cover all needs of the Business Project Management.

+ Provide Teamwork with a cloud-based repository where members and projects are organized and accessible.

+ CMMN Diagram Tool provide link for CMMN with BPMN and DMN.

+ Export CMMN diagrams as images in PNG, PDF, JPG and SVG formats.

- Visual Paradigm does not have an execution CMMN model engine yet.

+ Turn your diagrams into flexible and reusable models that can be versioned.

+ Check correctness and validate your models against the standard specification.

+ Export your models using the standard interchange file format.

+ Augment your models with documentation and multimedia content and then animate them.

+ Collaborate and share your work with the rest of your organization via modern Web Browsers.

+ Can define extra setting to a task as lag time process time, cost setting, value, quality and more in order analyzing work efforts.

+ The teamwork ribbon allows you to work with the collaborative features in the Case Modeler.

+ You can get access to manage your repository options.

+ You can and add and view posted comments made by others and yourself.

+ You can also access the versioning capabilities to define the state and version of your models and share your model with others via the share function.

+ Models defined with the open standards BPMN, CMMN and DMN.

Table - 9 Summarizing the top three CMMN tool’s Evaluation.

Page: 83

7.5 CMNN Tools Evaluation against KiPs Requirements

Requirements Rank 9-10 CMMN tool specification provides Full Coverage, 7-8 CMMN tool specification provides Partial Coverage,

5-6 CMMN tool specification provides Basic Coverage, 0 CMMN tool specification does not provide Support. - CMMN specification does not provide support

Flo

wab

le

Vis

ual

Par

adig

m

Tris

ote

ch

Flo

wab

le

Vis

ual

Par

adig

m

Tris

ote

ch

Data Modelling Execution

R1 Data modelling R2 Late Data Modelling R3 Access to appropriate data R4 Synchronized access to shared data

7 5 5 0

8 6 6 0

8 6 6 0

- 7 8 8

- 0 0 0

- 8 8 8

Knowledge Actions Modelling Execution

R5 Represent data-driven actions R6 Late actions modelling

10 9

10 10

10 10

- 9

- 0

- 10

Rules And Constraints Modelling Execution

R7 Formalize rules and constraints R8 Late constraints formalization

5 -

5 -

5 -

- 6

- 0

- 5

Goals Modelling Execution

R9 Goals Modelling R10 Late Goal Modelling

9 -

9 -

9 -

- 10

- 0

- 9

Process Modelling Execution

R11 Support for different modelling styles R12 Visibility of the process knowledge R13 Flexible process execution R14 Deal with unanticipated executions R15 Migration of process instances R16 Learning from event logs R17 Learning from data sources

- - - - - - -

- - - - - - -

- - - - - - -

- 9

10 10 10 9

10

- 0 0 0 0 0 0

- 9

10 10 9 9 9

Knowledge Workers Modelling Execution

R18 Knowledge workers’ modelling R19 Formalize interaction between knowledge workers R20 Define knowledge workers’ privileges R21 Late knowledge workers’ modelling R22 Late privilege modelling R23 Capture knowledge workers’ decision

6 - 8 - - -

6 - 8 - - -

6 - 8 - - -

- 8 8 5 5 9

- 0 0 0 0 0

- 8 7 5 5 9

Environment Modelling Execution

R24 Capture and model external events R25 External events late modelling

10 -

10 -

10 -

9 9

0 0

9 9

Table - 10 CMMN Tool Evaluation

Page: 84

Table-10, presents the evaluation of top three CMMN tools, Flowable, Visual Paradigm and Trisotech. We

have four ranking levels. CMMN specification provides Full Coverage (9-10), CMMN specification provides

Medium Coverage (7-8), CMMN specification provides Basic Coverage (5-6) and CMMN specification does

not provide Support (0).

The first requirement of KiPs is the Data Modelling. All CMMN modelling tools specification provide a

medium coverage on Data Modelling. CMMN uses mainly the Case File Item to represent all data and data

structures. All the case file items are stored in the case file. Case file items are used to represent all kinds of

data, including a data value in a database, a row in a database, a document, a spreadsheet, a picture, a video,

a voice recording, etc. In addition to basic data, case file items can also represent containers, including, a

directory, a folder, a set, a stack, a list, etc. Flowable has a rank of 7 in contrast to Visual Paradigm and

Trisotech that have 8 ranks. The reason is that Flowable does not support Case File Item in his toolbox that

is a basic data element for the CMMN notation language. Instead of that it has a Case Page to support the

creation of the case file items but in practise a case page is used to model work that needs to be done by a

human actor. The main difference with a human task is that no task is created for a case page task, only a

plan item instance. Case File Item is more generic and, in my opinion, it is more suitable data modelling

representative.

According Late Data modelling all CMMN modelling tools specification provide a Basic rank as we referring on modelling all three CMMN tool has similar behavior. At execution CMMN modelling tools specification provide a medium rank. Visual Paradigm has does not support CMMN execution engine at the moment and it cannot be ranked. Trisotech is bit more flexible in handling new knowledge at run-time and it can involve in more easy way at creation/modification of new/existing data. Flowable and Trisotech give capability to the end-users to add new data to the information model during the process enactment, or to alter or remove the existing ones. This enables modellers to control the execution of a process, and to extract patterns, as the end-users keep on adding information in the form of tasks and attributes. At access to appropriate data, all CMMN tools during modelling offers a basic implementation. All CMMN tools can offer a connection between data and process. In our example we have the case file item that represent the data base data that stored for the greenhouse and represent the greenhouse status, the other tasks process and human tasks, to retrieve information from it and to update it as well. On execution CMMN during execution offers a medium implementation. Flowable and Trisotech are flexible on access relevant data at any point of process enactment to those participants having the required authorizations. Visual Paradigm has does not support CMMN execution engine at the moment and it cannot be ranked. On Synchronized access to shared data requirement all CMMN modelling tools specification provide a medium rank. Flowable and Trisotech have a solid collaboration platform that support multiple users or multiple tasks to access and alter the same data concurrently at the same time, without the risk of affecting the integrity of data.

According Represent data-driven actions all CMMN modelling tools specification provide a high rank. Data-driven" model is defined as a model that is an approximation of an objects connecting the system state variables (input, internal and output), often is based only a limited knowledge about the "physical" behaviour of the system, usually uses machine learning and data mining methods as the basis and depending on Knowledge Worker experience. An information model including all relevant data manipulated by the process and their interrelationships is required. Data can be more or less accurate, and may refer to different levels of abstraction, ranging from detailed properties provided by process variables to more aggregate information stored in data objects, which hold information structures pertinent to the global context. In our smart farming example all smart sensor technology input data to our model, building data objects that are connected with different system process and build an information that the system machine learning growing

Page: 85

the knowledge database and in combination with the Knowledge-Worker decisions the system is capable of taking actions.

In late actions modelling all CMMN modelling tools specification provide a high rank. Flowable has evaluated a rank of nine instead of ten as the Visual Paradigm and Trisotech is ranked, because it does not support discretionary tasks, plans and stages which in a sense present the nature of a process as unpredictable or as emergent. CMMN provides the concept of discretionary items that can be added at execution time to the case plan by case workers and can be considered late actions modelling. In our case study we have the “Emergent Critical Event Expert Handling” as a discretionary task that it is only run if a weather condition emergency take place. In this case a Knowledge Worker must gather all the information about the emergency and will create a Critical Emergency Report. Flowable, and Trisotech CMMN execution provide a high rank in dealing with the “emergent nature” of a KiP, end-users can add new knowledge actions to the process instance during its enactment, or to alter the existing ones.

Formalize rules and constraints refers on the concept, that in a KiP, the existence of policies, rules and regulations can influence the process structure and constrain its execution. To this end, an end-user must be allowed to explicitly define constraints or business rules on process data. All CMMN modelling tolls specification provide a basic rank on security access to data, formalization of rules and constraints, resource and skill modelling, and the definition of worker privileges. All CMMN tools provide the ability to have entry criterion that describes the condition that must be satisfied for the stage, task, or milestone to be available for execution and exit criterion that is similar to an entry criterion, but it is used to stop working on the stage, task, or case (case plan) when it is satisfied. Stage, task, or milestones without entry criteria will be available for execution as soon as they are created. Entry and exit criterion are based on events from either the information model or the behavioural model, and are used to guide the execution of the model. Finally, all CMMN tools specification provide the capability to define the basic rules for a task (Manual Activation Rule, Required Rule, Auto Complete and Repetition Rule). CMMN provides a concept of roles, but it is not related with the information model. For access to data, the case worker environment could use the role concept to impose access control over the case data.

According with Late Constraints Formalization, Flowable and Trisotech, allow the end-users to add new constraints at run-time, or to alter the existing ones, when new data or actions emerge during process enactment. On Goals Modelling CMMN does not provide a global case goal definition, however it provides milestones

that can be used to track intermediate goals. Visual Paradigm, Flowable and Trisotech CMMN modelling

tools specification provide this capability in equal good way. Our greenhouse case study has made a great

utilization of this feature because it has several critical milestones that triggers most important processes in

our case (prompt watering milestone, normal conditions milestone, prompt open Rooftop milestone,

prompt close rooftop milestone, critical emergency report milestone, open roof milestone and close roof

milestone).

According with Late Goal Modelling, Flowable and Trisotech provide the mechanism to participants to associate new goals to a running process or to alter/remove existing goals that have become outdated in order to adapt new process goals that may arise as a result of knowledge workers' decisions or due to the evolution of data & knowledge elements. At the requirement of the Support for different modelling styles, CMMN does not provides support for different modelling styles, because it is a declarative style of modelling. However, a CMMN implementation could include BPMN and in doing so provide both declarative and procedural modelling styles, (Marin, M.A., Hauder, M.,Matthes, F., 2015).

Page: 86

According to Visibility of the process knowledge, Flowable and Trisotech supports an aggregated perspective of data, actions, constraints and goals involved in a running process, including their state as well as their interdependencies. At Flexible process execution requirement, Flowable and Trisotech have an excellent engine that provide a very strong collaboration environment between the participants, which can decide during the execution phase to change the order of steps in the process and the type of information needed. Participants can step back or jump forward, to re-execute previously performed actions, or to skip actions deemed unnecessary in a given instance.

In Deal with unanticipated executions, Flowable and Trisotech have executable engines to catch

unanticipated exceptions and provide mechanisms to generate the recovery procedure dealing with such

exceptions, which are either manual or completely automated, depending on the specific case. They both

maintain historic and runtime tables with activity, variables, execution data etc. They have a very satisfying

logging system and debugging environment.

At Migration of process instances, when process definitions are updated with new versions, the question

arises what should be done with already running process instances that are using older versions of the

process definition. In case running process, instances should be migrated to another process definition

version, you can use the process instance migration features. Flowable Engine and Trisotech engine

support:

✓ Cases for process instance migration overview.

✓ Automatic migration of wait states (user task, receive task, intermediate catch events) to activities with the same id in the new process definition version.

✓ Manual migration of wait states by specifying the target activity for a specific active state in the running process instance.

✓ Migrating a wait state to an activity with a boundary timer, signal or message event.

✓ Migrating a wait state with a boundary timer, signal or message event to an activity without a boundary event.

✓ Migrating a wait state to an activity in an embedded sub process or a nested embedded sub process.

✓ Migrating a wait state in an embedded sub process or a nested embedded sub process to the root level of the process definition or another nested scope.

✓ Migrating a wait state to an activity in an (nested) event sub process, both interrupting and non-interrupting.

✓ Migrating multiple executions when using a parallel or inclusive gateway, to one execution outside of the gateway scope.

✓ Migrating from a single execution to multiple executions within a parallel or inclusive gateway.

✓ Migrating a wait state to an activity in the parent process.

Page: 87

At Learning from event logs requirement Flowable and Trisotech has made a very serious work on learning from previous executed instances/cases. They are maintaining records of event logs that trace the process progression. A learning activity based on event logs may help to understand the impact of a KiP in real world, discover the KiP's process model, or check whether a pre-specified model is conformant with the event logs. It may also result in an improvement of the information model, in the definition of new actions. Learning from data sources Flowable and Trisotech have the capability to gather knowledge from heterogeneous data sources, in order to discover or improve the structure of a KiP.

At Knowledge workers’ modelling Flowable, Visual Paradigm and Trisotech provide equal the ability to define a resource model including multiple participants with multiple roles/capabilities. Roles serve as a means of grouping knowledge workers with similar duties. Capabilities are used for specifying whether a knowledge worker provides the required skills to execute a specific action. Case and Stages are the CMMN element that can serve this modelling requirement. In our case study we have four different stages for managing the knowledge workers according their roles, duties and skills. We have the Irrigation System stage, Rooftop Management stage, Watering Plants stage and the Air Purification System stage. At the Formalize interaction between knowledge workers Flowable and Trisotech has collaborative platform that let knowledge workers who play different roles to collaborate during the process enactment with many methods (chatting environment, live video conference etc.) during the model execution. On Defining knowledge workers’ privileges Flowable, Visual Paradigm and Trisotech provide equal at modelling environment. On the execution Flowable and Trisotech can define knowledge workers’ privileges by assigning specific task for every participant separately and specifying permissions for creating/altering/deleting data and knowledge elements, avoiding that confidential information is made available to inappropriate knowledge workers. According Late knowledge workers’ modelling Flowable and Trisotech provide during execution phase the concepts of manual activation for tasks and stages, and manual completion for stages and the case instance itself that support the emergent characteristic. Emergent nature of a KiP, it could be required to insert new knowledge workers and their respective capabilities to the resource model at run-time, to alter capabilities of existing knowledge workers or to remove existing knowledge workers from the resource model. According Late privilege modelling Flowable and Trisotech provide during execution phase the capability of adding, removing, altering privileges associated to existing knowledge workers, since new knowledge entities may arise. On Capture knowledge workers’ decision, Flowable and Trisotech, can capture decisions made by knowledge workers and affects the process progression or the state of information model during run-time and to associate their occurrence's impact on the process progression and on the information model. The Capture and model external events requirement Flowable, Visual Paradigm and Trisotech provide equal at modelling environment of supporting external events. In our case study we have event listener to bring the information of the measurements of smart sensors that the greenhouse consists of and associate their occurrence's impact on the information model. At External events late modelling, Flowable and Trisotech can capture if a new external event (that was not previously captured) occurs and let the knowledge worker to formalize it and associate its occurrence impact on the information model.

Page: 88

8. Conclusion In this dissertation, we conducted a comparative study of CMMN tools to support the knowledge process:

The case of smart farming. We have chosen the field of smart farming because it is an innovative industry

that constantly incorporates the latest technologies and is constantly evolving into a technologically evolving

industry that changes processes and practices to keep up with the new digital world of internet of things.

We have examined in depth the Knowledge-intensive Processes (KiPs). Because the new digital world of

I.O.T. consist mainly by such process. We found out that Knowledge-intensive processes require flexibility

and scalability in modelling, as well as profound integration of data and decisions into the process. We

emphasize that those processes are strongly dependent on knowledge workers performing various

interconnected knowledge intensive decision-making tasks. We examine in detail the main characteristics as

well as their requirements. The characteristics and requirements of KiPs make us reconsider the classical

process life cycle based on the design, execute and monitor, analyze and re-design.

We understand that the hieratically procedural centricity model is turn to a knowledge worker and event

driven centricity model. We end up at the conclusion that data-centric approaches represent a promising

solution for supporting new environment of I.O.T. and case management practices. Case Management tend

to excels at complex and long-running business processes that require a mix of human and digital actions.

Common use cases include incident management, customer and employee on-boarding, or handling

incoming applications, claims, complaints or incident management in general. Case Management Model and

Notation CMMN is the core modelling language of Object Management Group that is recommended in

Business Process Management. The true meaning of CMMN language is definitely not to replace BPMS

language. The case management empowers CMMN to orchestrate process or process-fragments, events,

their conditions and the actions follow, data pulled into or entered to the case, documents uploaded to the

case or created by a process, people involved in a case and services involved in a case.

We evaluated three representative CMMN business suits by installing them and explore their possibilities

and new features that enclose on notation modeling based mainly to the CMMN language. To accomplish

that we examine a case study of smart farming that implement all the characteristics and the majority of

requirement of KiPs process. We examine one by one the tools in order to examine the complexity that have

added in notation modeling and measure the modeling background that demands in order to utilize the

advanced characteristics of that tools. We explored the capabilities of their execution environments on

model execution, collaboration, on their versioning and debugging system. We identified their differences

between them, the strengths and weaknesses.

The tools that support CMMN language have grown heavily enough and evolve every year. We have already

complete integrated suits that hold in one connected environment all the Object Management Group

business projects. All projects are related to each other, can transfer information between them and are

parts of one great modeling solution. They already have a complete design platform that connects CMMN

with BPMS and DMN and some of them have a very solid engine for the model’s execution as well. The

executions engines are fully collaborated oriented they can maintain historical logs between executions,

they provide a very solid debugging environment for handling exceptions. They have versioning mechanism

that can return in a previous modelling stage if it is necessary. CMMN make BPMS smarter and engaging. It

is essential to combine CMMN, BPMN and DMN in one runtime.

Flowable, Visual Paradigm and Trisotec are excellent in the designed environment with a great variety of

elements and characteristics that gives the capability to design any model. They have a very powerful

teamwork environment that gives the opportunity to participants to collaborate and work simultaneously,

Page: 89

exchange ideas and proposals. They can make changes at the same time without worries about losing or

messing their work. Flowable and Trisotec have very satisfying and solid execution environment for their

models. Visual Paradigm is expected to implement its own CMMN execution engine at the next versions of

the product.

Page: 90

References

Aalst, W.M.P.V.D., Berens, P.J.S. (2001). Beyond Workflow Management: Product-Drive Case Handling. In:

Proceeding of the 2001 International ACM SIGGROUP. ACM Press, New York, pp. 42-51.

Aalst, W.M.P.V.D., Weske, M., Grunbauer, D.: Case Handling. (2005). A New Paradigm for Business Process

Support. Data and Knowledge Engineering, pp. 129-162.

Backman J, Oksanen T, Visala A. (2013). Applicability of the ISO 11783 network in a distributed combined

guidance system for agricultural machines Original Research Article. Biosyst Eng 114(3):, pp. 306-

317.

Berkley, J.D., Eccles, R.G. (1991). Rethinking the Corporate Workplace: Case Managers at Mutual Benefit

Life. Case N9-492-015, Harvard Business School, Boston, MA.

Boehlje MD, Eidman VR. (1984). Farm management. Wiley, New York, p. 806.

Bohringer, M. (2011). Emergent Case Management for Ad-hoc Processes: A Solution Based on

Microblogging and Activity Streams. In: zur Muehlen, M., Su, J. (eds.) Business Process Management

Workshops, Lecture Notes in Business Information Processing, vol. 66, Springer Berlin Hei, pp. 384-

395.

BPMN.IO. (n.d.). Retrieved from https://bpmn.io/toolkit/cmmn-js/.

BPTrends. (2009). Case Management: Combining Knowledge With Process. BPTrends, www.bptrends.com.

Camunda. (n.d.). Retrieved from https://camunda.com.

Clair, L.C., Moore, C., Vitti, R. (2009). Dynamic Case Management An Old Idea Catches New Fire. Tech. rep.,

Forrester, Cambridge, MA.

Claudio Di Ciccio, Andrea Marrella, Alessandro Russo. (2015). Knowledge-intensive Processes

Characteristics, Requirements and Analysis of Contemporary Approaches.

Daberkow SG, McBride WD. (2003). Farm and operator characteristics affecting the awareness and

Adoption of precision agriculture technologies in the US. Precis Agric 4(2):, pp. 163-177.

Dalmaris P, Tsui E, Hall B, Smith B. (2007). A framework for the improvement of knowledgeintensive

business processes. BPM Journal 13(2):, pp. 279-305.

Davenport TH. (2005). Thinking for a Living: How to Get Better Performance and Results from Knowledge

Workers. Harvard Business Rev. Press.

Davenport TH. (2005). Thinking for a Living: How to Get Better Performance and Results from Knowledge

Workers. Harvard Business Rev. Press.

Davenport, T., Nohria, N. (1994). Case Management and the Integration of Labor. MIT Sloan Management

Review 35(2), 1123.

de Man, H., Prasad, S., van Donge, T. (2010). Mastering the Unpredictable: How Adaptive Case

Management Will Revolutionize the Way That Knowledge Workers Get Things Done, chap.

Innovation Management, Meghan-Kiffer Press, Tampa, Florida, USA, 1st edn., pp. 211-255.

DEEHAN, N. (2020). https://camunda.com/blog/2020/08/how-cmmn-never-lived-up-to-its-potential/.

Page: 91

F. Hasic, J. Vanthienen. (2019). Complexity metrics for DMN decision models.

Farmstar. (n.d.). Retrieved from https://www.farmstar-conseil.fr.

Flowable. (n.d.). Retrieved from https://flowable.com/.

Fountas S, Carli C, Sorensen CG, Tsiropoulos Z, Cavalaris C, Vatsanidou A, Liakos B, Canavari M,

Wiebensohn J, Tisserye B. (2015a). Farm management information systems: current situation and

future perspectives. Comput Electron Agric 115:, pp. 40-50.

Fountas S, Ess D, Sorensen CG, Hawkins S, Blumhoff G, Blackmore S, Lowenberg-DeBoer J. (2005). Farmer

experience with precision agriculture in Denmark and the US eastern cornbelt. Precis Agric 6, pp.

121-141.

Fountas S, Sorensen CG, Tsiropoulos Z, Cavalaris C, Liakos V, Gemtos T. (2015b). Farm machinery

management information system. Comput Electron Agric 110:, pp. 131-138.

Fountas S, Wulfsohn D, Blackmore S, Jacobsen HL, Pedersen SM. (2006). A model of decision making and

information flows for information-intensive agriculture. Agric Syst 87, pp. 192-210.

Gladwin H (1989) . (n.d.). Ethnographic decision tree modelling. Sage Publications Ltd., London Hines T

(2000) An evaluation of two qualitative methods (focus group interviews and cognitive maps) for

conducting research into entrepreneurial decision making.Qual Mark Res Int J 3(1):, pp. 7-16.

Gronau N, Muller C, Uslar M. (2004). The KMDL Knowledge Management Approach: Integrating Knowledge

Conversions and Business Process Modeling. In: Practical Aspects of Know. Management.

Hauder, M., Kazman, R., Matthes, F. (2015). Empowering End-Users to Collaboratively Structure Processes

for Knowledge Work. In: Proceedings of the 18th International Conference on Business Information

Systems (BIS).

Heiser, J., Lotto, R.J.D. (2007). Introduction to Investigative Case Management Products. Tech. Rep. April,

Gartner, Stamford, CT.

I. Routis, M. Nikolaidou, D. Anagnostopoulos. (2020). Empirical evaluation of CMMN models: a

collaborative process case study, Springer-Verlag GmbH Germany, part of Springer Nature.

Isik O, Van den Bergh J, Mertens W. (2012). Knowledge Intensive Business Processes: An Exploratory Study.

In: 45th Hawaii International Conference on System Science, HICSS '12, pp. 3817-3826.

Kaan, K., Reijers, H.a., Molen, P.V.D. (2006). Introducing Case Management: Opening Workflow

Managements Black Box. In: Dustdar, S., Fiadeiro, J., Sheth, A. (eds.) BPM, pp. 358-367.

Kempenaar C, van Evert FK, Been T, Kocks CG, Westerduk CE. (2016). Towards data-intensive, more

sustainable farming: advances in predicting crop growth and use of variable rate technology in

arable crops in the Netherlands. ICPA.

Kerremans, M. (2008). Case Management Is a Challenging BPMS Use Case. Tech. Rep. December, Gartner,

Stamford, CT, gartner research number G00162739.

La Rosa M, Mendling J. (2008). Domain-driven processcess adaptation in emergency scenarios. In: Business

Process Management Workshops, Springer, pp. 290-297.

Lewis T. (1998). Evolution of farm management information systems. Comput Electron Agric 19, pp. 233-

248.

Page: 92

Leymann F, Roller D. (2000). Production Work: Concepts and Techniques. Prentice Hall.

M Chinosi, A. T. (2012). Computer Standards & Interfaces.

M von Rosing, S. W. (2015). Business Process Model and Notation BPMN. In S. W. M von Rosing.

M. Pankowska. (2019). Procedia Computer Science - Business Models in CMMN, DMN and ArchiMate

language.

Magne MA, Cerf M, Ingrand S. (2010). A conceptual model of farmers’ informational activity: a tool for

improved support of livestock farming management. Animal 4:, pp. 842-852.

Marin, M. A. (2016). Introduction to the Case Management Model and Notation (CMMN).

Marin, M.A., Hauder, M.,Matthes, F. (2015). Case management: an evaluation of existing approaches for

knowledge-intensive processes. In: InternationalConference on Business ProcessManagement,

Springer, pp. 5-16.

Marjanovic O, Freeze R. (2011). Knowledge Intensive Business Processes: Theoretical Foundations and

Research Challenges. In: Proc. of the 44th Hawaii Int. Conf. on Sys. Sc., HICSS '11.

Marjanovic O, Skaf-Molli H, Molli P, Godart C. (2007). Collaborative Practice-Oriented Business Processes {

Creating a New Case for Business Process Management and CSCW Synergy. In: CollaborateCom.

McCauley, D. (2010). Mastering the Unpredictable: How Adaptive Case Management Will Revolutionize the

Way That Knowledge Workers Get Things Done, chap. Achieving Agility, Meghan-Kiffer Press,

Tampa, Florida, USA, 1st edn., pp. 257-275.

Mike A. Marin, Matheus Hauder, Florian Matthes. (2015). Case Management: An Evaluation of Existing

Approaches for Knowledge-Intensive Processes.

Motahari-Nezhad, H.R., Swenson, K.D. (n.d.). Adaptive Case Management: Overview and Research

Challenges. In: Proceedings of the 2013 IEEE 15th Conference on Business Informatics. pp. 264{269.

CBI '13, IEEE Computer Society, Washington, DC, USA (2013),

http://dx.doi.org/10.1109/CBI.2013.44.

Mundbrod N, Kolb J, Reichert M. (2013). Towards a System Support of Collaborative Knowledge Work. In:

BPM Workshops, LNBIP, vol 132, Springer Berlin Heidelberg, pp. 31-42.

Murakami E, Saraiva AM, Ribeiro Junior LCM, Cugnasca CE, Hirakawa AR, Correa PLP. (2007). An

infrastructure for the development of distributed service-oriented information systems for

precision agriculture. Comput Electron Agric 58(1), pp. 37-48.

Nikkila R, Seilonen I, Koskinenet K. (2010). Software architecture for farm management information

systems in precision agriculture. Comput Electron Agric 70(2), pp. 328-336.

Object Management Group. (2016). Retrieved from Case management model and notation v1.1.:

http://www.omg.org/spec/CMMN/1.1/CMMN

Palmer, N. (2010). Mastering the Unpredictable: How Adaptive Case Management Will Revolutionize the

Way That Knowledge Workers Get Things Done, chap. Introduction Meghan-Kiffer Press, Tampa,

Florida, USA, 1st edn. , pp. 1-4.

Page: 93

Pucher, M.J. (n.d.). The Differnce between DYNAMIC and ADAPTIVE (November 2010),

https://acmisis.wordpress.com/2010/11/18/the-difference-between-dynamic-and-adaptive/,

accessed: April 29, 2015.

Reichert M. (2011). What BPM Technology Can Do for Healthcare Process Support. In: Proc. of the 13th

Conf. on AI in Medicine, AIME'11, pp. 2-13.

Reichert M, Rinderle S, Kreher U, Dadam P. (2005). Adaptive Process Management with ADEPT2. In:ICDE,

pp. 1113-1114.

Reichert M, Weber B. (2012). Enabling Flexibility in Process-Aware Information Systems - Challenges,

Methods, Technologies. Springer.

Reijers, H.A., Rigter, J., Aalst, W.M.P.V.D. (2003). The Case Handling Case. International Journal of

Cooperative Information Systems, pp. 365-391.

Signavio. (n.d.). Retrieved from https://www.signavio.com/.

Soren Marcus Pedersen, Kim Martin Lind. (2017). Progress in Precision Agriculture: Technoloy & Economic

Prespectives, pp. 181-200.

Sorensen GC, Fountas S, Nash E, Pesonen L, Bochtis D, Pedersen SM, Basso B, Blackmore SB. (2010).

Conceptual model of a future farm management information system. Comput Electron Agric 72, pp.

37-47.

St. Louis Lawson LG, Pedersen SM, Sorensen CG, Pesonen L, Fountas S, Werner A, Oudshoorn FW, Herold L,

Chatzinikos T, Kirketerp IM, Blackmore S. (2011). A four nation survey of farm information

management and advanced farming systems: a descriptive analysis of survey responses. Comput

Electron Agric 77:, pp. 7-20.

Swenson KD. (2010). Mastering the Unpredictable: How Adaptive Case Management Will Revolutionize the

Way That Knowledge Workers Get Things Done.

Swenson, K.D. (2013). State of the Art in Case Management. white paper, Fujitsu.

Trisotech Digital Modeling Suite. (n.d.). Retrieved from https://www.trisotech.com/.

Tsiropoulos Z, Fountas S, Gemtos T, Gravalos I, Paraforos D. (2013b). Management information system for

spatial analysis of tractor-implement draft forces. Precis Agric 13:, pp. 349-356.

Vaculin R, Hull R, Heath T, Cochran C, Nigam A, Sukaviriya P. (2011). Declarative business artifact centric

modeling of decision and knowledge intensive business processes. In: 15th IEEE Int Conf on

Enterprise Distr. Object Computing (EDOC 2011).

Visual Paradigm. (n.d.). Retrieved from https://www.visual-paradigm.com/.

White, M. (2009). Case Management: Combining Knowledge With Process. Tech. rep., BPTrends.