EcoKnow: Effektiv, Compliant og samskabt digitalisering af vidensarbejde

Effectiveco-created &

compliant adaptive case management forKnow-ledge workers

Roald Als

Thomas Hildebrandt Associate Professor IT University of Copenhagen

Infinit Seminar Processes & IT Århus, November 2nd, 2017

Grand Solutions

EcoKnow Presentation - Infinit Seminar - Nov 2nd Thomas Hildebrandt, [email protected]

IT UNIVERSITY OF COPENHAGEN

A single slide about me• 2000: PhD in computer Science, Århus University

• 1999-: Researcher & teacher at IT University of Copenhagen (ITU) in “digitalisation of processes”

• 2007-: Head of research & innovation projects in collaboration with Microsoft, Resultmaker, Exformatics, BaneDanmark, DSB, KL, BRFkredit,…

• 2012 - Head of research group at ITU, interest groups for digitalisation within infinit.dk, cfir.dk and videndanmark.dk and private digitalisation consultant

2

mailto:[email protected]

http://infinit.dk

http://cfir.dk

http://videndanmark.dk



Digitalised Case Management

3

RPA

BPM

Case Worker

Goal: Effectiveness and higher quality




Towards automation?

4

~27% of all tasks in public services in DK potentially automatable

A FUTURE THAT WORKS:the impact of automationin Denmark

A STRONGER AND MORE SECURE DIGITAL DENMARK Digital Strategy 2016-2020

The Government / LocaM Government Denmark / Danish Regions /

May 2016

Effective & trustworthy digital public services, freeing case workers from routine tasks & exploiting data to improve processes to the benefit of citizens




Today: Procedural automation

5

However, the focus is not on data but on process-related information (e.g., theordering of activities). Process mining is also related to monitoring and businessintelligence [41].

8 ConclusionProcess-aware information systems (PAISs) follow a characteristic life-cycle. Fig-ure 13 shows the four phases of such a life-cycle [7]. In the design phase, theprocesses are (re)designed. In the configuration phase, designs are implementedby configuring a PAIS (e.g., a WFMS). After configuration, the enactment phasestarts where the operational business processes are executed using the system con-figured. In the diagnosis phase, the operational processes are analyzed to identifyproblems and to find things that can be improved. The focus of traditional work-flow management (systems) is on the lower half of the life-cycle. As a result thereis little support for the diagnosis phase. Moreover, support in the design phase islimited to providing an editor while analysis and real design support are missing.

Figure 13: PAIS life-cycle.

In this article, we showed that PAISs support operational business processesby combining advances in information technology with recent insights from man-agement science. We started by reviewing the history of such systems and thenfocused on process design. From the many diagramming techniques available, wechose one particular technique (Petri nets) to show the basics. We also emphasizedthe relevance of process analysis, e.g., by pointing out that 20 percent of the morethan 600 process models in the SAP reference model are flawed [24]. We also

26

Ny medarbejder

Virk

som

hed

FM

FM

Find plads vedskrivebord

Skrivebords-nummer

1 uge før første arbejdsdag

Placer PC påbord

God første arbejdsdag

Håndtering af PC

PC type kan ikke leveres

PC type

HR

HR

Ret til PC?

kontrakter

Behov for PC?

Modtagunderskrevet

kontrakt

Arkiver kontrakt

IT Leverandør

beho

v fo

r PC

Nej

Ja





5





26

Only known and predictable routes are describedNy medarbejder

Virk

som

hed

FM

FM


Skrivebords-nummer


Placer PC påbord


Håndtering af PC


PC type

HR

HR

Ret til PC?

kontrakter

Behov for PC?

Modtagunderskrevet

kontrakt

Arkiver kontrakt

IT Leverandør

beho

v fo

r PC

Nej

Ja





5





26


Virk

som

hed

FM

FM


Skrivebords-nummer


Placer PC påbord


Håndtering af PC


PC type

HR

HR

Ret til PC?

kontrakter

Behov for PC?

Modtagunderskrevet

kontrakt

Arkiver kontrakt

IT Leverandør

beho

v fo

r PC

Nej

Ja

Introduce unnecessary dependencies

ITUNIVERSITYOFCOPENHAGEN

Towards effective, flexible & legally compliant digital knowledge workflows ITU, Sept 30, 2016

Thomas T. Hildebrandt ([email protected])

Thecomputersaysno….

5

Performance goals

Best practice

the computer says no

The baby is coming!





5





26


Virk

som

hed

FM

FM


Skrivebords-nummer


Placer PC påbord


Håndtering af PC


PC type

HR

HR

Ret til PC?

kontrakter

Behov for PC?

Modtagunderskrevet

kontrakt

Arkiver kontrakt

IT Leverandør

beho

v fo

r PC

Nej

Ja

Only describe the procedure (how), not why






5

Performance goals

Best practice


The baby is coming!





5





26


Virk

som

hed

FM

FM


Skrivebords-nummer


Placer PC påbord


Håndtering af PC


PC type

HR

HR

Ret til PC?

kontrakter

Behov for PC?

Modtagunderskrevet

kontrakt

Arkiver kontrakt

IT Leverandør

beho

v fo

r PC

Nej

Ja

Only describe the procedure (how), not why






5

Performance goals

Best practice


The baby is coming!

Difficult to maintain and update when regulations and best practice changes




arbejdsgangsbanken.dk

6

• Lov om Aktiv beskæftigelsesindsats

(LBK nr 1428 af 14/12/2009)

• Lov om Aktiv socialpolitik

(LBK nr 946 af 01/10/2009)

• Lov om Arbejdsløshedsforsikring

(LBK nr 574 af 27/05/2010)

• Lov om Integration af udlændinge

(LBK nr 1062 af 20/08/2010)

• Lov om Sygedagpenge

(LOV nr 563 af 09/06/2006)

• Retssikkerhedsloven

(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)

Compliant?

2010: Case Studies of Best Practice Workflow and Workflow in Practice (Innovation Network Project)


http://arbejdsgangsbanken.dk




6


(LBK nr 1428 af 14/12/2009)


(LBK nr 946 af 01/10/2009)


(LBK nr 574 af 27/05/2010)


(LBK nr 1062 af 20/08/2010)


(LOV nr 563 af 09/06/2006)


(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)

Change in law!Compliant?







6


(LBK nr 1428 af 14/12/2009)


(LBK nr 946 af 01/10/2009)


(LBK nr 574 af 27/05/2010)


(LBK nr 1062 af 20/08/2010)


(LOV nr 563 af 09/06/2006)


(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)

Change in law! Process change??Compliant?







6


(LBK nr 1428 af 14/12/2009)


(LBK nr 946 af 01/10/2009)


(LBK nr 574 af 27/05/2010)


(LBK nr 1062 af 20/08/2010)


(LOV nr 563 af 09/06/2006)


(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)



Process change!






6


(LBK nr 1428 af 14/12/2009)


(LBK nr 946 af 01/10/2009)


(LBK nr 574 af 27/05/2010)


(LBK nr 1062 af 20/08/2010)


(LOV nr 563 af 09/06/2006)


(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)



Process change!

Still compliant?





The Process Spectrum

7





7

Completely predictable Highly repetitive & objective e.g. preparing a meeting, handling routine application





7


Completely unpredictable Little repetitive,

explorative, unknown rules e.g. visiting a family for

the first time





7




the first time

Partially predictable, repetitive but varying routes

following known rules e.g. handling application for

economic support





7




the first time



economic support

Business Process Management (BPM) Robotic Process Automation (RPA)





7




the first time



economic support

Business Process Management (BPM) Robotic Process Automation (RPA) Electronic Case &

Document Management (ESDH)





7




the first time



economic support

Business Process Management (BPM) Robotic Process Automation (RPA) Electronic Case &

Document Management (ESDH)“Fagsystemer”




Ex: Child with disabilities1. Parents discover child has a permanent disabilities that

causes extra expenses (e.g. special equipment in house)

2. Parents apply for economic support (§ 41)

3. Parents need to reduce working time and receive compensation (§ 42)

4. Parents get extra help some days a month (§ 84)

5. Child turns 18 and case changes to adult regulations

8




Challenges today• It-support only for some activities (e.g. payments)

• No standard way of recording the case history

• Limited support for navigating the law

• Limited systematic sharing and use of knowledge

• The law changes

• The case worker changes

• The needs of the citizens changes

9


The Core IdeaRigid Procedural Business

Process Management Systems

“the computer says no (or leaves you on your own)”

Flexible Prescriptive Declarative Adaptive Case Management

“the computer says why and guides you on the way”



Key research questions• How can we digitalise the law such that it can be

maintained effectively and support case management?

• How can we gather and use data to help both citizens and case workers to find the best route?

11

5. Innovative value, impact and relevance of the project (max 0.5 page)

The finance and IT sectors are significant sectors in Denmark as measured by employment and innovation. Processes and usability is a position of strength in Denmark1 with a strong research environment and a number of successful companies, including SimCorp. The challenges of process aware IT systems and usability as U-CAPACITY addresses are not only important challenges to the financial sector, but to the society as a whole and a challenge the Danish ICT Growth Team recommends the government and companies address to strengthen the Danish competitiveness2. Indeed, they estimate, that the gross value added to the Danish businesses would be between 2,6 and 6,5 billion DKK, if the share of Danish businesses with at least one digitalized process is increased by 1%, and states as a goal for 2020 an increase by 10%. At the same time, the report also points to a recent study showing that the Danish businesses are seriously lacking behind the US when it comes to use and integration of IT in the organisation, in particular for SME’s. For ComBine in particular, U-CAPACITY contributes to the preservation of jobs outside the Cph region, increase SME’s use of ICT for digitalization and to increase the share of e-commerce directed to Danish shops: The statistics for first quarter of 2014 show that nordic consumers shopped in total for 8,31 billion DKK outside the Nordic countries while 70% of the Danish consumers did shop on the internet, also about 70% of the orders went to e-shops outside Denmark. With the strong consortium, international collaboration and timely objectives, it is anticipated that U-CAPACITY will have a clearly visible impact within all the involved research disciplines and more-over participate significantly to the goal of increasing digitalization of work processes in the Danish industry.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$1$Oxford$Research,$København)på)det)finansielle)verdenskort,$Copenhagen$2009:$http://www.cfir.dk/Forside/Dokumenter/Københavnpådetfinansielleverdenskort.aspx$$2$Vækstteam$for$IKT$og$digital$vækst,$ANBEFALINGER,$Januar$2014$$p.$31ff$Anbefaling)#3.)Et)nationalt)partnerskab)skal)øge)digitaliseringen)og)automatiseringen)af)særligt)små)og)mellemstore)virksomheder)betydeligt.$$

Side/Page 17 af/of 72

Municipality

ExformaticsKMD MAPS (Italy)

Copenhagen

Universit

y

DTU

David Basin, ETH Zurich, Switzerland

Institute of information Security

Marlon Dumas & Fabrizio Maria Maggi,

University of Tartu, Estonia

Hajo Reijers, VU University, Amsterdam

Municipality partnersas early adopters:

Koncern IT - Copenhagen Municipality

IT & Digitalisation,Syddjurs Municipality

Kammeradvokaten& Globeteam

EcoKnow: Effective, co-created & compliantadaptive case management for knowledge workers

Need for adaptable digitalisation of knowledge work processesChanging National and EU regulations (e.g. data protection)

Increased effectiveness and legal compliance

Enabling technologies shared as open source tools

via the OS2 open sourcedigitalisation community

01.10.2017 30.09.2021

Flexibility

Effective, co-created & compliantadaptive case management solutions

for knowledge workers

decisionsupport

Joos Buijs, Eindhoven Data Science Center

The Netherlands

Bank

Figur 1Side/Page 22 af/of 73





Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank






Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank






Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank





Key methods & disciplines• Field studies of work practices and research in

computer supported cooperative work (ITU & KU)

• Data & process mining for predictive and prescriptive process management (ITU, KU & KMD)

• Formal models of law and processes (ITU & Exformatics)

• Understandability studies of modelling tools and methods (DTU & KU)

12




Predictive Process Management

13

event logs





Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank


Outcomes

Knowledge work GPS




Predictive Process Management

13

event logs





Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank


Outcomes

How to predict theoutcome

given a partial traceof events?

Knowledge work GPS




Business Processes

14




Business Processes

14


(LBK nr 1428 af 14/12/2009)


(LBK nr 946 af 01/10/2009)


(LBK nr 574 af 27/05/2010)


(LBK nr 1062 af 20/08/2010)


(LOV nr 563 af 09/06/2006)


(LBK nr 1054 af 07/09/2010)

• Datagrundlag

(BEK nr 418 af 23/04/2010)




Events

15

case id: CPR of citizen + case name

data attributes: e.g. key figures in report 3

�1 = [(consultation, 1, 10:30AM, (age, 33), (gender, female), (amountPaid, 10), (department, radiotherapy)) . . .(ultrasound, 1, 10:55AM, (age, 33), (gender, female), (amountPaid, 15), (department, NursingWard))]

�2 = [(order blood, 2, 12:30PM, (age, 56), (gender, male), (department, GeneralLab) . . .(payment, 2, 2:30PM, (age, 56), (gender, male), (amountPaid, 100), (deparment, FinancialDept))]

Fig. 1: Extract of an event log.

to a problem of early sequence classification. In other words,given a set of labeled sequences, the goal is to build a modelthat for a sequence prefix predicts the label this prefix willget when completed. A survey on sequence classificationpresented in [7] provides an overview of techniques inthis field. This latter survey noted that, while there is sub-stantial literature on the problem of sequence classificationfor simple symbolic sequences (e.g. sequences of eventswithout payloads), there is a lack of proposals addressingthe problem for complex symbolic sequences (i.e. sequencesof events with payloads). The problem of outcome-orientedpredictive process monitoring can be seen as an early classi-fication over complex sequences where each element has atimestamp, a discrete attribute referring to an activity, and apayload made of a heterogeneous set of other attributes.

3 SEARCH METHODOLOGYIn order to retrieve and select studies for our survey andbenchmark, we conducted a Systematic Literature Review

(SLR) according to the approach described in [8]. We startedby specifying the research questions. Next, guided by thesegoals, we developed relevant search strings for querying adatabase of academic papers. We applied inclusion and ex-clusion criteria to the retrieved studies in order to filter outirrelevant ones, and last, we divided all relevant studies intoprimary and subsumed ones based on their contribution.

3.1 Research questionsThe purpose of this survey is to define a taxonomy ofmethods for outcome-oriented predictive monitoring of business

processes. The decision to focus on outcome-oriented predic-tive monitoring is to have a well-delimited and manageablescope, given the richness of the literature in the broader fieldof predictive process monitoring, and the fact that otherpredictive process monitoring tasks rely on entirely differenttechniques and evaluation measures.

In line with the selected scope, the survey focuses specif-ically on the following research question:

RQ0 Given an event log of completed business process exe-cution cases and the final outcome (class) of each case,how to train a model that can accurately and efficientlypredict the outcome of an incomplete (partial) trace,based on the given prefix only?

We then decomposed this overarching question into thefollowing subquestions:

RQ1 What methods exist for predictive outcome-orientedmonitoring of business processes?

RQ2 How to categorize these methods in a taxonomy?RQ3 What is the relative performance of these methods?

In the following subsections, we describe our approachto identifying existing methods for predictive outcome-oriented process monitoring (RQ1). Subsequent sectionsaddress the other two research questions.

3.2 Study retrieval

First, we came up with relevant keywords according to theresearch question of predictive outcome-oriented processmonitoring (RQ1) and our knowledge of the subject. Weconsidered the following keywords relevant:

• “(business) process” — a relevant study must take asinput an event log of business process execution data;

• “monitoring” — a relevant study should concern run-time monitoring of business processes, i.e. work withpartial (running) traces;

• “prediction” — a relevant study needs to estimate whatwill happen in the future, rather than monitor what hasalready happened.

We deliberately left out “outcome” from the set of key-words. The reason for this is that we presumed that differentauthors might use different words to refer to this predictiontarget. Therefore, in order to obtain a more exhaustive set ofrelevant papers, we decided to filter out studies that focuson other prediction targets (rather than the final outcome)in an a-posteriori filtering phase.

Based on these selected keywords, we constructed threesearch phrases: “predictive process monitoring”, “predictivebusiness process monitoring”, and “business process predic-tion”. We applied these search strings to the Google Scholaracademic database and retrieved all studies that containedat least one of the phrases in the title, keywords, abstract, orthe full text of the paper. We used Google Scholar, a well-known electronic literature database, as it encompasses allrelevant databases such as ACM Digital Library and IEEEXplore, and also allows searching within the full text of apaper.

The search was conducted in August 2017 and returned93 papers, excluding duplicates.

3.3 Study selection

All the retrieved studies were matched against several in-clusion and exclusion criteria to further determine their rel-evance to predictive outcome-oriented process monitoring.In order to be considered relevant, a study must satisfy allof the inclusion criteria and none of the exclusion criteria.

The assessment of each study was performed indepen-dently by two authors of this paper, and the results werecompared to resolve inconsistencies with the mediation of athird author.

3.3.1 Inclusion criteriaThe inclusion criteria are designed for assessing the rele-vance of studies in a superficial basis. Namely, these criteriaare checked without working through the full text of thepaper. The following inclusion criteria were applied to theretrieved studies:

Example: Jobcenter in municipality

Example: Hospital

An event have an activity name, a case id, a time-stamp and possibly some additional data attributes

Challenges: events distributed in many systems or not recorded name, case-id, time & data often not clear from logs




Traces & outcomes

16

Traces are finite sequences of events for the same case

Outcome function maps traces to outcomes

(may have several outcome functions)




Trace Encoders & Features

17

Number of cancelations, geographic area - i.e. the “shape” and characteristics of path

Trace encoders map partial traces to finite set of features

Challenges: What are the right features? Which features are ethical/fair to use ?

How to effectively extract features? Can features be identified automatically ?




Classifiers

18

(Known as early sequence classification)Classifiers map features to outcome predictions

Challenges: How to effectively train classifiers? How to explain predictions? How to use them?

FeaturesTrace encoder

outcome

classification




Process mining

19

Log files

https://youtu.be/7oat7MatU_U





Process mining

19

Log files

Statistically inferred process flow diagram






Process mining

19

Log files

Mining used for discovery, analysis, prediction, prescription

Statistically inferred process flow diagram






Mined diagrams often too complex

20

If the log has a lot of variation

the mined process quickly gets a complex spaghetti diagram





20



Failure to recognise concurrency





20



Failure to recognise concurrency

Inability of process language torepresent variation in a concise way




Declarative Process Mining

21

Declarative Process Mining for DCR Graphs⇤

Søren DeboisIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Thomas T. HildebrandtIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Paw Høvsgaard LaursenIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Kenneth Ry UlrikIT University of Copenhagen

Copenhagen, Denmark

[email protected]

ABSTRACTWe investigate process mining for the declarative DynamicCondition Response (DCR) graphs process modelling lan-guage. We contribute (a) a process mining algorithm forDCR graphs, (b) a proposal for a set of metrics quantifyingoutput model quality, and (c) a preliminary example-basedcomparison with the Declare Maps Miner. The algorithmtakes a contradiction-based approach, that is, we initiallyassume that all possible constraints hold, subsequently re-moving constraints as they are observed to be violated bytraces in the input log.

KeywordsDeclarative process mining; DCR graphs

1. INTRODUCTIONBusiness process management (BPM) technologies [33]

support the management and digitalisation of workflows andbusiness processes by employing explicit process models, fol-lowing a cycle of process (re)design, validation, executionand monitoring.Process mining algorithms [32] have been proposed for the

identification of process models from process logs, support-ing both process design and compliance monitoring.Most industrial BPM tools and process miners describe

processes as imperative flow diagrams such as BPMN. How-ever, flow diagrams tend to get either too rigid or too com-plex, in particular for knowledge work processes having ahigh degree of variation [28]. Moreover, flow diagrams onlydescribe how to perform a process, leaving a gap to the legalregulations and guidelines, that are often more declarative

in nature, describing why the process must be performed in

⇤Authors listed alphabetically. This work supported in partby the Velux Foundation, grant 33295, and ExformaticsA/S.

Permission to make digital or hard copies of part or all of this work for personal or

classroom use is granted without fee provided that copies are not made or distributed

for profit or commercial advantage and that copies bear this notice and the full citation

on the first page. Copyrights for third-party components of this work must be honored.

For all other uses, contact the owner/author(s).

SAC 2017 April 03-07, 2017, Marrakech, Moroccoc� 2017 Copyright held by the owner/author(s).

ACM ISBN 978-1-4503-4486-9/17/04.

DOI: http://dx.doi.org/10.1145/3019612.3019622

certain ways, not how exactly it must be performed. Forinstance, a clinical guideline may state, that a patient mustconsent to a blood transfusion [13]. It does not state ex-actly when such consent should be obtained, only “prior tothe transfusion”.For this reason, it is recommended to use flow diagrams

only for routine processes, or for describing common stan-dard practices and allow deviations [28]. It has been advo-cated that declarative notations should be used as output ofprocess mining (e.g. [17]) and for run-time process support(e.g. [25, 24, 29]). For the former, one hopes to extractfrom a process log the rules obeyed in practice (the “why”)as opposed to a flow-diagram describing the usual executions(the “how”). For the latter, one hopes to guide knowledgeworkers to activities in conformance with rules and regula-tions.Implementation techniques for most declarative models

such as Declare [27] and DecSerFlow [31], rely on translatingthe declarative constraints to an imperative model (e.g., anautomaton [20]) to enable execution. Such translation usu-ally entail a state-space explosion, and run-time adaptationof constraints becomes more di�cult, because the automa-ton must be recomputed when constraints change.A notable exception is the Dynamic Condition Response

(DCR) graphs process language [11, 30]. DCR graphs can beexecuted without intermediate transformation to an imper-ative model creating the entire transition graph, and moredirectly support run-time adaptive case management [24,5]. DCR graphs are supported by industrial design and casemanagement tools (see e.g. dcrgraphs.net and [5]).In the present paper, we present the first process mining

algorithm for DCR graphs.

2. DCR GRAPHSIn this Section, we briefly recall DCR graphs. For a formal

introduction and applications, refer to [11, 23, 30, 3, 5, 6].Dynamic Condition Response graphs is a declarative mod-

elling notation describing at the same time a process andits run-time state. The core notation comprises activities,activity states, and four relations between activities. An ac-tivity state comprises three booleans, indicating respectivelywhether the activity has been executed, is included, andis pending. Intuitively, activities that are not included aretreated as temporarily absent from the workflow; activitiesthat are pending must eventually be executed or excludedbefore the workflow may complete.

Presented for BPMEA track at 32nd ACM SAC 2017

condition response

Hypothesis: Constraints can be used as features real-time mining can be used as encoding




Declarative Process Mining

21

Statistically inferred process constraints

Declarative Process Mining for DCR Graphs⇤

Søren DeboisIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Thomas T. HildebrandtIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Paw Høvsgaard LaursenIT University of Copenhagen

Copenhagen, Denmark

[email protected]

Kenneth Ry UlrikIT University of Copenhagen

Copenhagen, Denmark

[email protected]

ABSTRACTWe investigate process mining for the declarative DynamicCondition Response (DCR) graphs process modelling lan-guage. We contribute (a) a process mining algorithm forDCR graphs, (b) a proposal for a set of metrics quantifyingoutput model quality, and (c) a preliminary example-basedcomparison with the Declare Maps Miner. The algorithmtakes a contradiction-based approach, that is, we initiallyassume that all possible constraints hold, subsequently re-moving constraints as they are observed to be violated bytraces in the input log.

KeywordsDeclarative process mining; DCR graphs

1. INTRODUCTIONBusiness process management (BPM) technologies [33]

support the management and digitalisation of workflows andbusiness processes by employing explicit process models, fol-lowing a cycle of process (re)design, validation, executionand monitoring.Process mining algorithms [32] have been proposed for the

identification of process models from process logs, support-ing both process design and compliance monitoring.Most industrial BPM tools and process miners describe

processes as imperative flow diagrams such as BPMN. How-ever, flow diagrams tend to get either too rigid or too com-plex, in particular for knowledge work processes having ahigh degree of variation [28]. Moreover, flow diagrams onlydescribe how to perform a process, leaving a gap to the legalregulations and guidelines, that are often more declarative

in nature, describing why the process must be performed in

⇤Authors listed alphabetically. This work supported in partby the Velux Foundation, grant 33295, and ExformaticsA/S.

Permission to make digital or hard copies of part or all of this work for personal or

classroom use is granted without fee provided that copies are not made or distributed

for profit or commercial advantage and that copies bear this notice and the full citation

on the first page. Copyrights for third-party components of this work must be honored.

For all other uses, contact the owner/author(s).

SAC 2017 April 03-07, 2017, Marrakech, Moroccoc� 2017 Copyright held by the owner/author(s).

ACM ISBN 978-1-4503-4486-9/17/04.

DOI: http://dx.doi.org/10.1145/3019612.3019622

certain ways, not how exactly it must be performed. Forinstance, a clinical guideline may state, that a patient mustconsent to a blood transfusion [13]. It does not state ex-actly when such consent should be obtained, only “prior tothe transfusion”.For this reason, it is recommended to use flow diagrams

only for routine processes, or for describing common stan-dard practices and allow deviations [28]. It has been advo-cated that declarative notations should be used as output ofprocess mining (e.g. [17]) and for run-time process support(e.g. [25, 24, 29]). For the former, one hopes to extractfrom a process log the rules obeyed in practice (the “why”)as opposed to a flow-diagram describing the usual executions(the “how”). For the latter, one hopes to guide knowledgeworkers to activities in conformance with rules and regula-tions.Implementation techniques for most declarative models

such as Declare [27] and DecSerFlow [31], rely on translatingthe declarative constraints to an imperative model (e.g., anautomaton [20]) to enable execution. Such translation usu-ally entail a state-space explosion, and run-time adaptationof constraints becomes more di�cult, because the automa-ton must be recomputed when constraints change.A notable exception is the Dynamic Condition Response

(DCR) graphs process language [11, 30]. DCR graphs can beexecuted without intermediate transformation to an imper-ative model creating the entire transition graph, and moredirectly support run-time adaptive case management [24,5]. DCR graphs are supported by industrial design and casemanagement tools (see e.g. dcrgraphs.net and [5]).In the present paper, we present the first process mining

algorithm for DCR graphs.

2. DCR GRAPHSIn this Section, we briefly recall DCR graphs. For a formal

introduction and applications, refer to [11, 23, 30, 3, 5, 6].Dynamic Condition Response graphs is a declarative mod-

elling notation describing at the same time a process andits run-time state. The core notation comprises activities,activity states, and four relations between activities. An ac-tivity state comprises three booleans, indicating respectivelywhether the activity has been executed, is included, andis pending. Intuitively, activities that are not included aretreated as temporarily absent from the workflow; activitiesthat are pending must eventually be executed or excludedbefore the workflow may complete.

Presented for BPMEA track at 32nd ACM SAC 2017

condition response

Hypothesis: Constraints can be used as features real-time mining can be used as encoding




Key methods & disciplines• Field studies of work practices and research in

computer supported cooperative work (ITU & KU)

• Data & process mining for predictive and prescriptive process management (ITU, KU, KMD)

• Formal models of law and processes (ITU, Exformatics)

• Understandability studies of modelling tools and methods (DTU & KU)

22


Example: Resourceforløb• “Stk. 3. Forud for visitation til et ressourceforløb skal den

forberedende del af rehabiliteringsplanen, jf. § 30 a, være udarbejdet og sagen have været forelagt rehabiliteringsteamet, jf. §§ 9-12 i lov om organisering og understøttelse af beskæftigelsesindsatsen m.v.

• Beskriver: - aktiviteter, e.g., (a) “visitationen” eller (b) “rehabiliteringsplanen [...] [skal] være udarbejdet”, samt - relationer imellem aktiviteter, e.g., “forud for [a] skal [b]”.

• “Stk. 3. Forud for visitation til et ressourceforløb skal den forberedende del af rehabiliteringsplanen, jf. § 30 a, være udarbejdet og sagen have været forelagt rehabiliteringsteamet, jf. §§ 9-12 i lov om organisering og understøttelse af beskæftigelsesindsatsen m.v.”

• “Stk. 3. Forud for visitation til et ressourceforløb skal den forberedende del af rehabiliteringsplanen, jf. § 30 a, være udarbejdet og sagen have været forelagt rehabiliteringsteamet, jf. §§ 9-12 i lov om organisering og understøttelse af beskæftigelsesindsatsen m.v.”

“The isomorphism principle”

• “Stk. 3. Forud for visitation til et ressourceforløb skal den forberedende del af rehabiliteringsplanen, jf. § 30 a, være udarbejdet og sagen have været forelagt rehabiliteringsteamet, jf. §§ 9-12 i lov om organisering og understøttelse af beskæftigelsesindsatsen m.v.

Change

Project OrganisationSteering Partners

(ITU, KU, DTU, KMD, Exformatics)Advisory Board

(DSC/e, TU Wien, KL, IF)WP4: Understandability PhD at DTU





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WP2: Data & Process Mining Industry PostDoc KMD & ITU





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Contributing partners (Core-technology, validation, adoption & outreach):

R&D Work Packages:

WP3: Legal Compliance Industry PostDoc Exformatics & ITU





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WP1: Work Practices PhD at ITU & Associate Prof at KU





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Dissemination Collaborators:

Baseline studies

R&D cycle 1 LAB

TRL&SRL4-5

R&D cycle 2 Relevant Env. TRL&SRL 6-7

Evaluation Operational Env

TRL&SRL 8Other markets

TRL&SRL 9

The 4 WorkPackages overall follow same structure:M6:Mar 2018 M18:Mar 2019

Agile, Situated Research & Design Annual international workshop

Innovation conference in 2019 and 2021 (infinit.dk, cfir.dk )

Implementation

M30:Mar 2020Sept 2017 M42:Mar 2021

Aug 2021

KMD A/S

Side 1 af 1 Ej nummereret v0.0 1. januar 2014 NA/NA I gang

Eskil Thygesen, Business Line Director, KMD, 49 years. . Education. Mannaz Executive Leadership Education (VL), 2012. HD(O), 2011, Copenhagen Business School. Cand.Jur, 1994, Aarhus University. Employment record (selected). 2016 – present: KMD, Business Line Director 2014-2016: Formpipe, VP Sales and Marketing 2006-2014: Schultz, VP Sales 2004-2006: Schultz, Chief executive, legal information division 2000-2004: Schultz, Head of sales division 1994-1997: Schultz, Head of operation unit Professional profile Eskil Thygesen has a strong working experience from leadership, management, product innovation, development, strategic sales and marketing, and expertise from knowledge-based industries from previous positions at KMD, Formpipe and Schultz. Eskils passion is to innovate, develop and implement powerful and user-friendly digital solutions for the public sector in Denmark and Europe. Projects (selected) 2016: Commercial and project responsible for the development of KMD’s new Jobcenter-solution to 70+ Danish municipalities. 2014-216: Commercial responsible for the development of Formpipes new ECM-system to 30+ Danish municipalities. 2012-2014: Commercial and project responsible for KOMBIT’s new self-service solution Byg&Miljø, which digitizes and streamline the building permit process in all 98 Danish municipalities. 2011-2014: Commercial responsible for the development of a new case management system to the Danish municipality-based jobcenters. The system is successfully implemented in 20+ Danish municipalities.


KMD A/S

Side 1 af 1 Ej nummereret v0.0 1. januar 2014 NA/NA I gang

Eskil Thygesen, Business Line Director, KMD, 49 years. . Education. Mannaz Executive Leadership Education (VL), 2012. HD(O), 2011, Copenhagen Business School. Cand.Jur, 1994, Aarhus University. Employment record (selected). 2016 – present: KMD, Business Line Director 2014-2016: Formpipe, VP Sales and Marketing 2006-2014: Schultz, VP Sales 2004-2006: Schultz, Chief executive, legal information division 2000-2004: Schultz, Head of sales division 1994-1997: Schultz, Head of operation unit Professional profile Eskil Thygesen has a strong working experience from leadership, management, product innovation, development, strategic sales and marketing, and expertise from knowledge-based industries from previous positions at KMD, Formpipe and Schultz. Eskils passion is to innovate, develop and implement powerful and user-friendly digital solutions for the public sector in Denmark and Europe. Projects (selected) 2016: Commercial and project responsible for the development of KMD’s new Jobcenter-solution to 70+ Danish municipalities. 2014-216: Commercial responsible for the development of Formpipes new ECM-system to 30+ Danish municipalities. 2012-2014: Commercial and project responsible for KOMBIT’s new self-service solution Byg&Miljø, which digitizes and streamline the building permit process in all 98 Danish municipalities. 2011-2014: Commercial responsible for the development of a new case management system to the Danish municipality-based jobcenters. The system is successfully implemented in 20+ Danish municipalities.


Prod

ucts

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http://infinit.dk

http://cfir.dk

Success Criteria

File number:7050-00034A

Page 6 of 52

digitalisation services.

The value created for citizens is the experienced and actual quality effectiveness and compliance of the case processing, which will be measured by

1) questionnaires to the experienced quality of case management before and after the introduction of EcoKnow technology,

2) reducing the number of complaints by citizens to Ankestyrelsen5 that leads to a change of decision

3) reducing of the wasteful waiting time

The success criterion is to achieve at least 15% increase in quality on all these measures.

The value created for case workers and municipalities is (in addition to the value created for the citizens) the increased experienced quality and actual effectiveness of the case management process, measured by


2) reduction of time by case workers spend on routine tasks 3) the experienced and actual ability of case workers and lawyers to efficiently on

their own digitalise and continuously adapt digitalised legal regulations and best practice workflows, utilising sharing between case workers within and across municipalities

4) reduction in the cost of adaptation of digitalised work processes in response to changing requirements.

The success criterion is to achieve at least 15% increase in quality on the first 3 criteria and an increase of at least 50% for the last.

The value created for the providers of digitalisation services (in addition to the value created for the citizens and their customers) is measured by increased market penetration and revenue during and until four years after the project completion and expansion to other markets as detailed below.

Value proposition and exit strategy

As described in the success criteria above, EcoKnow generates value in the form of:

1.Sales of products and services by KMD, Exformatics, Globeteam & Kammeradvokaten 2.Efficiency gains for citizens and case workers and reduced digitalisation costs for municipalities

Indirectly, the Danish citizens and society will gain value through much more effective governmental processes.

Ad 1: The business model of KMD and Exformatics is to embed the developed

5 http://ast.statistikbank.dk/statbank5a/selecttable/omrade0.asp?SubjectCode=77&PLanguage=0&ShowNews=OFF

File number:7050-00034A

Page 6 of 52

digitalisation services.

The value created for citizens is the experienced and actual quality effectiveness and compliance of the case processing, which will be measured by


2) reducing the number of complaints by citizens to Ankestyrelsen5 that leads to a change of decision

3) reducing of the wasteful waiting time

The success criterion is to achieve at least 15% increase in quality on all these measures.

The value created for case workers and municipalities is (in addition to the value created for the citizens) the increased experienced quality and actual effectiveness of the case management process, measured by


2) reduction of time by case workers spend on routine tasks 3) the experienced and actual ability of case workers and lawyers to efficiently on

their own digitalise and continuously adapt digitalised legal regulations and best practice workflows, utilising sharing between case workers within and across municipalities

4) reduction in the cost of adaptation of digitalised work processes in response to changing requirements.

The success criterion is to achieve at least 15% increase in quality on the first 3 criteria and an increase of at least 50% for the last.

The value created for the providers of digitalisation services (in addition to the value created for the citizens and their customers) is measured by increased market penetration and revenue during and until four years after the project completion and expansion to other markets as detailed below.

Value proposition and exit strategy

As described in the success criteria above, EcoKnow generates value in the form of:

1.Sales of products and services by KMD, Exformatics, Globeteam & Kammeradvokaten 2.Efficiency gains for citizens and case workers and reduced digitalisation costs for municipalities

Indirectly, the Danish citizens and society will gain value through much more effective governmental processes.

Ad 1: The business model of KMD and Exformatics is to embed the developed

5 http://ast.statistikbank.dk/statbank5a/selecttable/omrade0.asp?SubjectCode=77&PLanguage=0&ShowNews=OFF





Municipality


Copenhagen

Universit

y

DTU















01.10.2017 30.09.2021

Flexibility



decisionsupport


The Netherlands

Bank


1.09.2017 30.08.2021

Please contact me if you want to know more/get involved!

EcoKnow: Effektiv, Compliant og samskabt digitalisering af vidensarbejde

Technology

Transcript of EcoKnow: Effektiv, Compliant og samskabt digitalisering af vidensarbejde