Mining Resource Assignments and Teamwork Compositions from Process Logs

Stefan Schonig1, Cristina Cabanillas2, Claudio Di Ciccio2, Stefan Jablonski1, Jan Mendling2

1University of Bayreuth, Germany{stefan.schoenig, stefan.jablonski}@uni-bayreuth.de

2Vienna University of Economics and Business, Austria{cristina.cabanillas, claudio.di.ciccio, jan.mendling}@wu.ac.at

Abstract. Process mining aims at discovering pro-cesses by extracting knowledge from event logs. Suchknowledge may refer to different business processperspectives. The organisational perspective deals,among other things, with the assignment of humanresources to process activities. Information about theresources that are involved in process activities can bemined from event logs in order to discover resource as-signment conditions. This is valuable for process anal-ysis and redesign. Prior process mining approaches inthis context present one of the following issues: (i)they are limited to discovering a restricted set of re-source assignment conditions; (ii) they are not fullyefficient; (iii) the discovered process models are dif-ficult to read due to the high number of assignmentconditions included; or (iv) they are limited by the as-sumption that only one resource is responsible for eachprocess activity and hence, collaborative activities aredisregarded. To overcome these issues, we present anintegrated process mining framework that provides ex-tensive support for the discovery of resource assign-ment and teamwork patterns.

1 Introduction

Business process management is a well acceptedmethod for structuring the activities carried out inan organisation, analysing them for efficiency andeffectiveness, and identifying potential for improve-ment [6]. Processes are not always explicitly defined,which calls for a means to discover the implicit rulesaccording to which the processes are executed. Pro-cess mining provides different methods, among oth-ers, for automatically discovering processes by ex-tracting knowledge from event logs in the form of aprocess model. Various algorithms are available todiscover models capturing the control flow of a pro-cess [1]. For other perspectives, specifically involvinghuman resources1, only partial solutions for mininghave been developed, even though resource informa-tion is not only important for performance but alsofor compliance analysis [3, 4]. There is a need to bet-ter support this organisational perspective by min-ing resource-related process aspects. This is acknowl-edged by different approaches that mine this perspec-tive [10, 14]. Works in this area, however, focus onspecific aspects of the organisational perspective suchas role models, separation of duty or social networks.

1We use the term ”resource” to refer to ”human resources”.

None of the prior works offers a comprehensive andintegrated support for the well-established workflowresource patterns [11] and insights into the interplaybetween organisational and control-flow aspects [8],also known as cross-perspective patterns. In [13] weaddressed this research gap by developing an inte-grated declarative process mining approach for theorganisational perspective. It supports (i) all cre-ation aspects of the workflow resource patterns and(ii) cross-organisational patterns. Figure 1 illustratesits subdivision into an event log pre-processing phase,a phase for integratedly mining resource assignmentsas well as cross-perspective patterns, and a modelpost-processing phase.

The mining of resources is usually restricted by theassumption that individual activities are performed byexactly one person. However, in domains like health-care, software development, and knowledge-intensiveprocesses in general, most of the activities are car-ried out collaboratively, such that several human re-sources are involved with working on a single activ-ity. Domains in which collaborative work is frequentcan greatly benefit from mining team compositions,which unveil the capabilities and organisational re-lations of the team members [5]. Such approachesfor mining collaborative work in business processesare currently missing. In [12], we addressed this re-search gap by extending the proposed declarative pro-cess mining framework [13] towards the integration ofcollaborative activities. The approach comprises twosteps: it first extracts the teams participating in acollaborative activity from an event log and then dis-covers the overall characteristics of the team membersin terms of the skills, organisational roles, etc., thatare present in the team; afterwards, a two-step post-processing phase derives the most informative teamcompositions including the distribution of the discov-ered characteristics among the team members.

The approches for mining individual resource as-signments and teamwork have been evaluated in termsof (i) feasibility, by means of a proof-of-concept im-plementation; (ii) performance, by conducting simu-lation experiments; and (iii) applicability, by usingthe approaches on real-life event logs.

This paper is structured as follows: Section 2 intro-duces the target expressiveness of our mining frame-work as well as the data sources we use. Section 3describes the target language we use to represent the

Pre-print copy of the manuscript published in journal

Discovery of resource assignment

rules and influence on control flow

Org. Model

Improving mining efficiency by

generating only reasonable

candidates

Improving understandability of

results by pruning redundant

rules

Event Log

Pre-Processing

Model

Post-Processing

Log

Integrated Rule-based

Resource Mining

Log

Workflow

Resource Patterns

Cross-perspective

Patterns

Figure 1: Efficient framework for discovering resource assignment rules for individual activities [13]

mining results. In Section 4 we describe our approachfor discovering resource assignment patterns. Section5 gives an overview of the teamwork mining approachand the paper is finally concluded in Section 6.

2 Fundamentals

The well-known workflow resource patterns [11] cap-ture the various ways in which resources are repre-sented and utilised in business processes. The creationpatterns are of specific interest to our research sincethey describe different ways in which resources canbe assigned to activities. These patterns, which willbe referred to as organisational patterns from now on,include: Direct Distribution, or the ability to spec-ify at design time the identity of the resource thatwill execute a task; Role-Based Distribution, or theability to specify at design time that a task can onlybe executed by resources that have a given role; Or-ganisational Distribution, or the ability to offer or al-locate activity instances to resources based on theirorganisational position and their organisational rela-tionship with other resources; Separation of Duties,or the ability to specify that two tasks must be al-located to different resources in a given process in-stance; Case Handling, or the ability to allocate allthe activity instances within a given process instanceto the same resource; Retain Familiar (a.k.a. Bind-ing of Duties), or the ability to allocate an activityinstance within a given process instance to the sameresource that performed a preceding activity instance;Capability-Based Distribution, or the ability to offeror allocate instances of an activity to resources basedon their specific capabilities; Deferred Distribution, orthe ability to defer the specification of the identity ofthe resource that will execute a task until run time;and History-Based Distribution, or the ability to of-fer or allocate activity instances to resources based ontheir execution history; Note that the creation pat-terns Authorisation and Automatic Execution are notin the list because they are not directly related to re-source assignment;

It has been identified that process control flows are

intertwined with dependencies upon resource charac-teristics [8]. For instance, sometimes an activity mustbe executed eventually before another one for specificresources but not for others. As an example, resourceswith a certain role (e.g., trainees) must always per-form a certain activity (e.g., double-check result) be-fore they can continue. This might not be required forother roles (e.g., supervisors). We call this patternRole-Based Sequence. A specific collection of suchcross-perspective patterns capturing these situationshas not been defined. Nonetheless, in general, theycan be specified by combining the aforementioned or-ganisational patterns with the control-flow patternsdescribed in [2]. The Resource-Based Response pat-tern, e.g., describes that for a specific resource a cer-tain activity has to eventually follow another activity.The organisational and the cross-perspective patternsconstitute the set of patterns to be discovered by ourframework.

Our mining approaches take as input (i) an eventlog, i.e., a machine-recorded file that reports on the ex-ecution of tasks during the enactment of the instancesof a given process; and (ii) organisational backgroundknowledge, i.e., prior knowledge about the roles, ca-pabilities and the membership of resources to organi-sational units, among others.

3 Target Language

The modelling language to represent the discoveredpatterns must offer the possibility to define (i) expres-sive organisational patterns and (ii) cross-perspectivepatterns. Two different representational paradigmsfor process models can be distinguished: proceduralmodels describe which activities can be executed nextin a process; declarative models define by means ofrules the execution constraints that the process has tosatisfy [2]. Current procedural languages like BPMNput a strong emphasis on control flow and assumeother perspectives to be specified separately. Cross-perspective patterns cannot be readily modelled [7].Declarative process modelling does not limit the num-ber of perspectives involved in the constraints defined.

Pre-print copy of the manuscript published in journal

However, a central shortcoming of existing languageslike Declare [2] is that they are not provided with thecapability to directly define the connection betweenthe process behaviour and other perspectives. We willuse DPIL [15] for modelling the output of the miningbecause it supports multiple perspectives includingthe behavioural and the organisational perspectives,as well as the interplay between them. DPIL is ex-pressive enough to cover the workflow patterns [15].Nonetheless, the concepts of our approach are genericso that other declarative languages could also be usedas long as they provided support for the modelling ofour target patterns.

In order to express organisational information,DPIL builds upon a generic organisational metamodel. It comprises the following elements: Identityrepresents agents that can be directly assigned to ac-tivities, i.e., both human and non-human resources.Group represents abstract agents that may describeseveral identities as a whole, e.g., roles or groups.Relation represents relations (RelationType) thatmay exist between these elements. Thus, relationslike ”a person is the boss of another person” or ”aperson belongs to a certain department” can easilybe modelled. In this context, relations are generallyirreflexive. A relation is irreflexive if an element can-not be in relation to itself. The supervisor relation,e.g., is irreflexive, since a person cannot be their ownsupervisor. In addition, some relations may be transi-tive. A relation is transitive if whenever an individuali1 is related to another individual i2 with that rela-tion, and i2 is in turn related to a third individual i3with the same relation, then i1 is also related to i3.For instance, the supervisor and delegate relations aretypically transitive because organisations are usuallyhierarchically structured.

DPIL provides a textual notation based on the useof macros to define reusable rules. For instance, thesequence(a, b) macro states that the existence of astart event of task b implies the previous occurrenceof a complete event of task a; and the role(a, r) macrostates that an activity a is assigned to a role r. Fig-ure 2 shows an example of a process for trip man-agement modelled with DPIL. It specifies that it ismandatory to approve a business trip before flighttickets can be booked. Moreover, it is necessary thatthe approval be carried out by a resource with the roleProfessor.

4 Analysing Resource Assignments

In this section we describe our approach to discoverorganisational and cross-perspective patterns. First,we describe how rule candidates are generated andchecked. Then, we classify them according to supportand confidence values. Finally, we present a catalogueof rule templates that covers the target expressiveness.

use group Professor

process BusinessTrip {task Book flighttask Approve Application

ensure role(Approve Application, Professor)ensure sequence(Approve Application, Book flight)

}

Figure 2: Process for trip management modelled withDPIL

4.1 Resource Assignment Rule Discovery

Declarative process modelling languages like DPILare based on so-called rule templates. A rule tem-plate captures frequently needed relations and definesa particular type of rules. Unlike concrete rules, arule template consists of placeholders, i.e., typed vari-ables. A rule template is instantiated by providingconcrete values for these placeholders. For instance,the model described in Fig. 2 makes use of two ruletemplates represented by the macros sequence(T1,T2)and role(T ,G). These templates comprise placehold-ers of type Task T as well as Group G. In all well-known declarative process mining approaches, ruletemplates are used for querying the provided eventlog to find solutions for the placeholders. A solutionis any combination of concrete values for the place-holders that yields an actual rule satisfied in the eventlog. First, all possible rules need to be constructed byinstantiating the given set of rule templates with allpossible combinations of occurring process elementsprovided in the event log. For example, the sequencetemplate consists of two placeholders of type Task.Assuming that |T | different tasks occur in the eventlog, |T |2 rule candidates are generated. The result-ing candidates are subsequently checked w.r.t. the log.Checking rule candidates as described above providesfor every candidate the number of instances, i.e., thetraces in the event log where it holds. Based on thesevalues it is possible to classify rules and to separatenon-valid from valid ones.

Maggi et al. [9] adopted different metrics, specif-ically support (supp) and confidence (conf ) forevaluating the relevance of rule candidates. Let |Φ|be number of traces in an event log Φ. Let |σnv(r)|be the number of traces in which a rule r : A → Bis satisfied. The support supp(r) and confidenceconf(r) values of a rule r are defined as:

supp(r) := |σnv (r)||Φ| , conf (r) := supp(r)

supp(A)

We make use of the confidence value to classifya rule candidate r as a valid rule (i.e., satisfiedin almost all traces) or a non-valid rule (i.e., vio-lated in most of the recorded traces). Therefore,the threshold minConf is introduced to classify rulecandidates. Candidates r with conf (r) ≥ minConfare classified as valid. All rule candidates r with

Pre-print copy of the manuscript published in journal

conf (r) < minConf are non-valid rules and are notpart of the resulting process model. Using the confi-dence values of rule candidates it is directly possibleto generate a DPIL process model reflecting organisa-tional and cross-perspecitve patterns.

4.2 Templates of Organisational Patterns

Since DPIL builds upon a flexible organisational metamodel, it is possible to define rule templates that de-scribe many aspects of the organisation. By instanti-ating these rule templates with all possible parametercombinations of defined resources, groups and relationtypes, it is possible to generate rule candidates thatfocus on the organisational perspective of the processto be analysed. These candidates can then be checkedunder consideration of the event log and the organi-sational model.

In the following we define rule templates and theirmacros for our target set of patterns. First of all,we distinguish between templates for organisationalpatterns and templates for cross-perspective patterns.The former are, in turn, divided into two groups basedon the types and number of parameters: rule tem-plates related to a single task and rule templates re-lated to more than one task. We provide representa-tive examples for each group of rule templates thatcover frequently needed organisational information.Note that, besides the templates described next, fur-ther templates could be defined individually to coverthe analyst’s needs.

The first group includes rule templates that defineorganisational patterns referred to one process activ-ity. The Direct Distribution pattern can be extractedwith the direct(T ,I) template. Given the free vari-ables T and I and an event log with |T | distinct tasksand |I| distinct resources, there are |T | · |I| candidatesto be checked.direct(T,I) iff start(of T) implies start(of T by I)

The Role-Based distribution pattern can be ex-tracted with the role(T ,G) template. Here, rule can-didates for every task and group combination are gen-erated, i.e., |T |·|G| rule candidates need to be checked.role(T,G) iff start(of T by :p) implies

relation(subject p predicate hasRole object G)

The Capability-Based distribution pattern can beextracted with the capability(T ,RT ,G) template. Acapability is represented by a relation of an individ-ual to a group, e.g., i1 hasDegree ComputerScience.According to the placeholders, |T | · |RT | · |G| candi-dates are generated.capability(T, RT, G) iff start(of T by :p) implies

relation(subject p predicate RT object G)

The assignment of resources based on organi-sational positions of individuals, described by theOrganisation-Based Distribution pattern, can be ex-tracted with the orgDistSingle(T ,RT ,G) template.Here, |T | · |RT | · |G| rules must be checked.

orgDistSingle(T, RT, G) iff start(of T by :p) implies

relation(subject p predicate RT object G)

The second group includes rule templates that de-fine organisational patterns referred to several tasks.The Separation of Duties pattern can be extractedwith the separate(T1,T2) template. For this template,|T |2 candidates need to be checked.separate(T1,T2) iff start(of T1 by :p) and start(of T2)

implies start(of T2 by not p)

The Retain Familiar pattern can be extracted withthe binding(T1,T2) template. Similarly to the previouscase, |T |2 candidates need to be checked.binding(T1,T2) iff start(of T1 by :p) and start(of T2)

implies start(of T2 by p)

The Case Handling pattern can be extracted withthe caseHandling template. Here, |T | candidates haveto be checked.caseHandling iff forall(task T start(of T) implies

start(of T by :p))

Resources can also be assigned to tasks accordingto their organisational relation with the performers ofother process activities, e.g., an approval task mightbe assigned to people that can supervise the work doneby the performers of a previous task. This is coveredby the Organisation-Based Distribution pattern andcan be extracted with the orgDistMulti(T1,T2,RT )template where variable RT specifies the type of re-lation between the two individuals involved. Thereexist |T |2 · |RT | rule candidates.orgDistMulti(T1,T2,RT) iff start(of T1 by :p1) and

start(of T2 by :p2) implies

relation(subject p1 predicate RT object p2)

A cross-perspective rule describes a temporal de-pendency or constraint between tasks but only ap-plies for a certain set of identities, like in the follow-ing examples. Note, that other well-known control-flow patterns described in [2] can be defined in a sim-ilar way. The Role-Based Sequence pattern can beextracted with the roleSequence(T1,T2,G) template.Here, |T |2 · |G| candidates need to be checked.roleSequence(T1,T2,G) iff start(of T2 by :p at :t) and

relation(subject p predicate hasRole object G)

implies complete(of T1 at < t)

5 Mining Teamwork Patterns

In this section, we describe our approach to automati-cally discover team compositions from event logs. Forpreparing and representing the mining output, we relyagain on the DPIL mining framework and the organ-isational rule templates. An example is depicted inFig. 3. From the first mining step three differentteams are extracted from an event log for a collabo-rative activity A. After analysing the teams againstthe existing background knowledge from the organi-sational model, the team characteristics role Doctor,role Nurse and capability Blood Test are discovered forthese teams. Finally, the post-processing phase con-

Pre-print copy of the manuscript published in journal

A1

A2

An

EVENT LOG ORGANISATIONAL MODEL

TEA

MW

ORK

MIN

ING

–CO

MPO

STIO

NS

BACK

GRO

UN

D K

NO

WLE

DG

EBLOOD TEST

DOCTOR

NURSE

TEAM 1

TEAM 2

TEAM N

TEAMWORK MINING – CHARACTERISTICS + POST-PROCESSING

A TEAMCHARACTERISTICS

• CONCRETE PERSON WHO IS A DOCTOR• AT LEAST ONE NURSE• AT LEAST ONE PERSON WHO CAN PERFORM BLOOD TESTS

RESOURCES

ROLE

ROLE

CAPABILITY

Figure 3: Approach for mining team compositions [12]

cludes that the teams performing that activity mustalways include a specific person ? who, in turn, is adoctor; at least one nurse; and at least one personthat can perform blood tests.

5.1 Extraction of Discriminative Teamsand their Characteristics

The first step is to extract the different sets of partici-pants that are needed to perform the different processinstances. For every trace we extract the set of dis-tinct individuals that are associated with at least oneof its events. The result of the scan of an event logwith n different traces is a set of distinct teams.

The set of frequent teams already provides valu-able insights into the operation of a business process.However, it does not help managers to learn how theycan compose teams in a potentially more effective way.Therefore, the next step of our approach focuses onthe mining of frequent team characteristics. To thisextent, we make again use of the DPIL mining frame-work and its concepts of rule templates with certainplaceholders. In contrast to our previous approach,which mines for resource assignment rules for each sin-gle task, here we abstract from the tasks and search forassignment rules that refer to a complete sub-process.Hence, the parameter referring to a task in the ruletemplates can be omitted. This way, the direct tem-plate, e.g., results in the following definition:direct(I) iff event() implies event(by I)

The approach is visualized in Fig. 4. It illustratesthe complete mining procedure by example direct androle rule candidates and different team compositions.For example, this means that a direct(i1) rule holdstrue for a certain process if individual i1 performs atleast one task in every instance of the collaborativeactivity. Therefore, mining teamwork aspects reducesthe number of candidates for the direct template to nIrules to be evaluated. Evaluating rule candidates asdescribed above yields for every candidate the number

of traces in the log where it holds. In order to judgethe relevance of the rules, we adopt similarly to Sec-tion 4 the support and confidence thresholds conceptsto evaluate the relevance of rule candidates.

5.2 Post-Processing

Extracting the characteristics of the team membersis not sufficient to describe how the team is actuallycomposed. In order to compose a new team it is nec-essary to know how the characteristics are distributedamong the team members. For instance, the results sofar only say that at least someone in every team hasthe extracted characteristics. However, it is not clearhow many persons in a team have a certain charac-teristic and which of these characteristics are maybefulfilled by one and the same person, i.e., overlappingcharacteristics. In the following, we briefly describetwo post-processing steps to define more precisely theway in which a team is composed for a collaborativeactivity. Each of these steps introduces precision butalso computational complexity.

In a first step we move from analysing at team levelto analysing at resource level. Specifically, we countfor each characteristic among those extracted for theteams the number of team members that fulfil it. Theoutcome is the minimum number of persons that fulfileach characteristic, i.e., a rule within a team. The re-sult is more informative than the initial one becauseit adds cardinality to the extracted characteristics.However, it is done only at an individual level, i.e.,for each single characteristic. Since one single personmay have several of the characteristics discovered, ina last step we consider all the possible combinationsof characteristics and we check each of them for everymember of the team. The outcome is the set of over-lapping rule sets with the number of resources thatmust have each set of characteristics within the team.This offers a more detailed view of the team compo-sition.

6 Conclusion and Outlook

In this paper we presented an integrated process min-ing framework to efficiently discover resource assign-ment patterns as well as team attributes and com-position patterns of collaborative activities in busi-ness processes. The framework builds upon a declar-ative process mining technique focusing on the pro-cess resource perspective. In [12, 13] we showed theapplicability and success of the approach with reallife event logs and case studies. In all application ar-eas we showed that resource mining provides inter-esting insights in the way resources are involved, howteams are composed and how collaborative work isperformed. In future work we want to apply our min-ing approaches on event logs from additional domainslike hospital logs, and use the results for different pur-poses, e.g., for checking compliance rules with respect

Pre-print copy of the manuscript published in journal

C1

DIRECT(i1)

DIRECT(i2)

DIRECT(i7)

ROLE(DOCTOR)

ROLE(NURSE)

ROLE(RM)

C2

DIRECT(i1)

DIRECT(i2)

DIRECT(i7)

ROLE(DOCTOR)

ROLE(NURSE)

ROLE(RM)

Cn

DIRECT(i1)

DIRECT(i2)

DIRECT(i7)

ROLE(DOCTOR)

ROLE(NURSE)

ROLE(RM)

TEAM 1 TEAM 2 TEAM N

RULE

CAN

DIDA

TES

… … … … … …

SUPPORT THRESHOLD

EVENT LOG

DIRECT(i1) ROLE(DOCTOR) ROLE(NURSE)

RULE

S

…

i1

i3

i7

i1

i2

i6

i1

i4

i6

i5

Figure 4: Checking of various organisational patterns in collection of resources [12]

to team compositions.

Acknowledgements

This research was funded by the Austrian ResearchPromotion Agency (FFG), grant 845638 (SHAPE).

References

[1] van der Aalst, W.: Process mining: discovery,conformance and enhancement of business pro-cesses. Springer-Verlag Berlin Heidelberg (2011)

[2] van der Aalst, W., Pesic, M., Schonenberg, H.:Declarative workflows: Balancing between flexi-bility and support. Computer Science - Researchand Development 23(2), 99–113 (2009)

[3] van der Aalst, W.M.P., Rosemann, M., Dumas,M.: Deadline-based escalation in process-awareinformation systems. Decision Support Systems43(2), 492–511 (2007)

[4] Cabanillas, C., Knuplesch, D., Resinas, M.,Reichert, M., Mendling, J., Ruiz-Cortes, A.:RALph: A Graphical Notation for Resource As-signments in Business Processes. In: CAiSE, vol.9097, pp. 53–68 (2015)

[5] Di Ciccio, C., Marrella, A., Russo, A.:Knowledge-intensive processes: Characteristics,requirements and analysis of contemporary ap-proaches. J. Data Semantics 4(1), 29–57 (2015)

[6] Dumas, M., Rosa, M.L., Mendling, J., Reijers,H.A.: Fundamentals of Business Process Man-agement. Springer (2013)

[7] Jablonski, S.: MOBILE: A modular workflowmodel and architecture. In: Working Conferenceon Dynamic Modelling and Information Systems(1994)

[8] de Leoni, M., van der Aalst, W.M., Dees, M.:A general process mining framework for correlat-ing, predicting and clustering dynamic behaviorbased on event logs. Information Systems 56,235–257 (2016)

[9] Maggi, F.M., Bose, J.C., van der Aalst, W.: Ef-ficient Discovery of Understandable DeclarativeProcess Models from Event Logs. In: CAiSE,pp. 270–285 (2012)

[10] Nakatumba, J., van der Aalst, W.: Analyzingresource behavior using process mining. In: BPMWorkshops, pp. 69–80 (2010)

[11] Russell, N., van der Aalst, W.M.P., ter Hofstede,A.H.M., Edmond, D.: Workflow Resource Pat-terns: Identification, Representation and ToolSupport. In: CAiSE, pp. 216–232 (2005)

[12] Schonig, S., Cabanillas, C., Di Ciccio, C., Jablon-ski, S., Mendling, J.: Mining Team Compositionsfor Collaborative Work in Business Processes.Software and Systems Modeling (2016)

[13] Schonig, S., Cabanillas, C., Jablonski, S.,Mendling, J.: A Framework for Efficiently Min-ing the Organisational Perspective of BusinessProcesses. Decision Support Systems 89, 87–97(2016)

[14] Song, M., Van der Aalst, W.M.: Towards com-prehensive support for organizational mining.Decision Support Systems 46(1), 300–317 (2008)

[15] Zeising, M., Schonig, S., Jablonski, S.: Towardsa Common Platform for the Support of Routineand Agile Business Processes. In: Collabora-tive Computing: Networking, Applications andWorksharing, pp. 94–103 (2014)

Pre-print copy of the manuscript published in journal

This document is a pre-print copy of the manuscript(Schönig et al. 2016)published in journal.

References

Schönig, Stefan, Cristina Cabanillas, Claudio Di Ciccio, Stefan Jablonski, and Jan Mendling (2016).“Mining Resource Assignments and Teamwork Compositions from Process Logs”. In: Softwaretechnik-Trends 36.4. url: http://pi.informatik.uni- siegen.de/stt/36_4/03_Technische_Beitraege/Schoenig_et_al_2016.pdf.

BibTeX@Article{ Schoenig.etal/STT2016:MiningResourceAssignmentsfromLogs,

author = {Sch{\"{o}}nig, Stefan and Cabanillas, Cristina and DiCiccio, Claudio and Jablonski, Stefan and Mendling, Jan},

title = {Mining Resource Assignments and Teamwork Compositions fromProcess Logs},

journal = {Softwaretechnik-Trends},year = {2016},volume = {36},number = {4},url = {http://pi.informatik.uni-siegen.de/stt/36_4/03_Technische_Beitraege/Schoenig_et_al_2016.pdf}

}