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Advanced Information Systems Engineering 29th International
Conference CAiSE 2017
Essen, Germany, June 12-16, 2017
Proceedings of CAiSE Forum and Doctoral Consortium Papers
Edited by
Xavier Franch
Universitat Politècnica de Catalunya, Spain
Jolita Ralyté University of Geneva, Switzerland
Raimundas Matulevičius University of Tartu, Estonia
Camille Salinesi University Paris 1 Panthéon Sorbonne,
France
Roel Wieringa University of Twente, The Netherlands
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CAiSE 2017 Forum and Doctoral Consortium Papers Proceedings
This volume of CEUR-WS Proceedings contains 20 Forum and 4
Doctoral Consortium papers presented at the 29th International
Conference on Advanced Information Systems Engineering (CAiSE
2017). The conference was held in Essen, Germany, June 12-16, 2017.
Copyright © 2017 for the individual papers by the papers’ authors.
Copying permitted only for private and academic purposes. This
volume is published and copyrighted by its editors. CEUR-WS.org,
ISSN 1613-0073
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CAiSE 2017 Forum Foreword
The objective of the CAiSE conferences is to provide a forum for
the exchange of experience, research results, ideas and prototypes
between the research community and industry, in the field of
information systems engineering. Along almost three decades, the
conference has become the yearly worldwide meeting point for the
information system engineering community. This year, the 29th
edition of the CAiSE conference is held in Essen, Germany, from the
12th to the 6th of June 2017.
One of the usual tracks in the CAiSE conference is the Forum,
and this year is not an exception. The Forum sessions facilitate
the interaction, discussion, and exchange of ideas among presenters
and participants. Intended to serve as an interactive platform, the
Forum aims at the presentation of emerging new topics and
controversial positions, as well as demonstration of innovative
systems, tools and applications. In accordance, two types of
submissions have been called to the Forum:
Visionary papers presenting innovative research projects, which
are still at a relatively early stage and do not necessarily
include a full-scale validation.
Demo papers describe innovative tools and prototypes that
implement the results of research efforts. The tools and prototypes
will be presented as demos in the Forum.
Each submission to the CAiSE’17 Forum was reviewed by three
Program Committee members. Only those submissions for which there
was an agreement on the relevance, novelty and rigor were accepted
for presentation in the Forum. Additionally, some papers were
invited to the Forum as a result of the evaluation process in the
main conference. All in all, there was a total of 20 papers that
were presented as part of the main conference program. The
presenters gave a 3-minute elevator pitch and were available to
discuss their work through a poster and/or system demonstration in
a dedicated session. The 8-page papers describing the works are
compiled in these proceedings.
We would like to thank everyone who contributed to CAiSE’17
Forum. First, to our excellent Program Committee members who
provided thorough evaluation of the papers and contributed to the
promotion of the event. We thank all the authors who submitted and
presented papers to the Forum for having shared their work with the
community. Last, we would like to thank the CAiSE’17 Program
Committee and General Chairs as well as the Local Organization
Committee for their support. June 2017
Xavier Franch Jolita Ralyté
CAiSE Forum Co-Chairs
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CAiSE’17 Forum Co-Chairs
Xavier Franch Universitat Politècnica de Catalunya, Spain Jolita
Ralyté University of Geneva, Switzerland
CAiSE’17 Forum Committee
Carina Alves Universidade Federal de Pernambuco, Brasil Said
Assar Institut Mines-Telecom, France Juan Pablo Carvallo CEDIA;
Universidad De Cuenca, Ecuador Dolors Costal Universitat
Politècnica de Catalunya, Spain Rébecca Deneckère Université Paris
1 Panthéon Sorbonne, France Deepak Dhungana Siemens, Austria
Christophe Feltus Luxembourg Institute of Science and
Technology,
Luxembourg Agnès Front University of Grenoble, France Smita
Ghaisas Tata, India Chiara Ghidini Fondazione Bruno Kessler, Italy
Irit Hadar University of Haifa, Israel Jennifer Horkoff University
of Gothenburg, Sweden Marta Indulska University of Queensland,
Australia Haruhiko Kaiya Kanagawa University, Japan Evangelia
Kavakli University of the Aegean, Greece Christian Kop
Alpen-Adria-Universitaet Klagenfurt, Austria Dejan Lavbič
University of Ljubljana, Slovenia Lysanne Lessard University of
Ottawa, Canada Emmanuel Letier University College London, UK Grace
Lewis Software Engineering Institute, USA Gilles Perrouin
University of Namur, Belgium Pilar Rodríguez University of Oulu,
Finland Marcela Ruiz Utrecht University, Netherlands Arnon Sturm
Ben-Gurion University of the Negev, Israel Gianluigi Viscusi EPFL,
Switzerland Yong Xia IBM China, China
Additional Reviewers
Faeq Alrimawi Sorren Hanvey Giulio Petrucci
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CAiSE 2017 Doctoral Consortium Foreword
The CAiSE 2017 Doctoral Consortium (DC) was the 24th Doctoral
Consortium of a series held in conjunction with the International
CAiSE conference. It brought together PhD students working on
foundations, techniques, tools and applications of Information
Systems Engineering and provided them with an opportunity to
present and discuss their research to an audience of peers and
senior faculty in a supportive environment. The CAiSE 2017 DC was a
unique opportunity to: − Get fruitful feedback and advice to the
selected Doctoral students on their
research project; − Meet experts from different backgrounds
working on topics related to the
Information Systems Engineering field; − Interact with other
Doctoral students and stimulate an exchange of ideas and
suggestions among participants; − Discuss concerns about
research, supervision, the job market, and other career-
related issues. The doctoral students involved were selected
after a careful evaluation process of
their papers by a couple of senior academics. Besides, the
common quality, originality and thematic criteria, candidates had
to have at least 6-12 months of work remaining before expected
completion (and at least 12 months of work already performed), so
as to fully benefit from the Doctoral Consortium. Based on the
recommendations provided by the mentors, papers were revised before
publication in the proceedings. A collection of 4 papers was
selected then presented at the meeting:
In Stage-based Business Process Mining Hoang Nguyen presented a
set of techniques for process mining at the process stages. The
major goals of the research were to extract business process stages
from the event logs, to mine process logs and to perform predictive
process monitoring at different process stages. The paper reports
on the preliminary results for discovering process stages and for
mining process logs based on the process stages.
Christian Fleig presented the paper Towards the Design of a
Process Mining-Enabled Decision Support System for Digital Business
Process Transformation. The author proposed the process-mining
enabled decision support systems to guide development and
transformations of the business processes.
Towards Operationalization of Business Models: Designing Service
Compositions for Service-Dominant Business Models by Bambang
Suratno considered transformations of the business process models
to the service compositions. A successful composition requires
understanding of the essential properties and guidance for the
service composition and execution.
Alex Mircoli in his presentation of Automatic Emotional Text
Annotation Using Facial Expression Analysis discussed the approach
to enrich textual information with the emotional aspects. The major
challenge of this study is to explain how to capture such an
information from the speech and video presentations.
Last but not least the CAiSE DC featured a short tutorial on the
research methods given by Prof. Roel Wieringa.
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We would like to thank warmly the DC mentors for their
dedication and advice to the doctoral students. We hope students
could fully benefit from all advices that were provided about their
papers and during the meeting, and we wish them a long and fruitful
career in research and higher education.
June 2017
Raimundas Matulevičius Camille Salinesi
Roel Wieringa CAiSE 2017 DC Co-Chairs
Doctoral Consortium Co-Chairs
Raimundas Matulevičius University of Tartu, Estonia Camille
Salinesi Université Paris 1 Panthéon Sorbonne, France Roel Wieringa
University of Twente, The Netherlands
Doctoral Consorium Mentors
Marite Kirikova Riga Technical University, Latvia Selmin Nurcan
Université Paris 1 Panthéon Sorbonne, France Oscar Pastor
Universitat Politècnica de Valencia, Spain Barbara Pernici
Politecnico di Milano, Italy Hans Weigand Tilburg University, The
Netherlands Jelena Zdravkovic Stockholm University, Sweden
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Table of Contents
CAiSE 2017 Forum Papers
A Data-Driven Approach to Improve the Process of Data-Intensive
API Creation and Evolution
Alberto Abelló, Claudia Ayala, Carles Farré, Cristina Gómez,
Marc Oriol, and Oscar Romero
1
Smart Logistics: An Enterprise Architecture Perspective Prince
M. Singh, Marten van Sinderen, Roel Wieringa
9
Enriching Business Artifacts with Coordination Matteo Baldoni,
Cristina Baroglio, Federico Capuzzimati, and Roberto Micalizio
17
EthDrive: A Peer-to-Peer Data Storage with Provenance Xiao Liang
Yu, Xiwei Xu, and Bin Liu
25
Hybrid Remote Expert - an Emerging Pattern of Industrial Remote
Support Ethan Hadar, Joseph Shtok, Benjamin Cohen, Yochay Tzur, and
Leonid
Karlinsky
33
XES Tensorflow – Process Prediction using the Tensorflow
Deep-Learning Framework
Joerg Evermann, Jana-Rebecca Rehse, and Peter Fettke
41
A Process Mining Based Model for Customer Journey Mapping Gaël
Bernard and Periklis Andritsos
49
VarMeR – A Variability Mechanisms Recommender for Software
Artifacts Iris Reinhartz-Berger and Anna Zamansky
57
Cloudy with a Chance of Usage? – Towards a Model of Cloud
Computing Adoption in German SME
Robert Deil and Philipp Brune
65
Model Fragment Reuse Driven by Requirements Raúl Lapeña, Jaime
Font, Carlos Cetina, and Óscar Pastor
73
Regerator: a Registry Generator for Blockchain An Binh Tran,
Xiwei Xu, Ingo Weber, Mark Staples, and Paul Rimba
81
Regression Testing for Visual Models Ralf Laue, Arian Storch,
and Markus Schnädelbach
89
Privacy Level Agreements for Public Administration Information
Systems Vasiliki Diamantopoulou, Michalis Pavlidis, and Haralambos
Mouratidis
97
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Artifact-driven Process Monitoring: Dynamically Binding
Real-world Objects to Running Processes
Giovanni Meroni, Claudio Di Ciccio, and Jan Mendling
105
GH4RE: Repository Recommendation on GitHub for Requirements
Elicitation Reuse
Roxana Lisette Quintanilla Portugal, Marco Antonio Casanova,
Tong Li, and Julio Cesar Sampaio do Prado Leite
113
Improving Problem Resolving on the Shop Floor by Context-Aware
Decision Information Packages
Eva Hoos, Pascal Hirmer, and Bernhard Mitschang
121
Information Logistics and Fog Computing: The DITAS* Approach
Pierluigi Plebani, David Garcia-Perez, Maya Anderson, David
Bermbach, Cinzia Cappiello, Ronen I. Kat, Frank Pallas, Barbara
Pernici, Stefan Tai, and Monica Vitali
129
Towards Multi-decision-maker Requirements Prioritisation via
Multi-Objective Optimisation
Fitsum Meshesha Kifetew, Angelo Susi, Denisse Muñante, Anna
Perini, Alberto Siena, and Paolo Busetta
137
An Empirical Evaluation to Identify Conflicts Among Quality
Attributes in Web Services Monitoring
Jael Zela Ruiz and Cecilia M. F. Rubira
145
Business Process Modelling for a Data Exchange Platform
Christoph Quix, Arnab Chakrabarti, Sebastian Kleff, and Jaroslav
Pullmann
153
CAiSE 2017 Doctoral Consortium Papers
Stage-based Business Process Mining Hoang Nguyen
161
Towards the Design of a Process Mining-Enabled Decision Support
System for Business Process Transformation
Christian Fleig
170
Towards Operationalization of Business Models: Designing Service
Compositions for Service-Dominant Business Models
Bambang Suratno
179
Automatic Emotional Text Annotation Using Facial Expression
Analysis Alex Mircoli
188
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Stage-based Business Process Mining
Hoang Nguyen1
Supervisors: Marcello La Rosa1, Marlon Dumas2 and Arthur H.M.
terHofstede1
1 Queensland University of Technology,
[email protected],
{a.terhofstede,m.larosa}@qut.edu.au
2 University of Tartu, Estonia{marlon.dumas}@ut.ee
Abstract. Evidence-based BPM has gained significant momentum
inrecent years, thanks to the widespread adoption of enterprise
systemsthat store detailed business process execution data in event
logs. Tech-niques for analyzing business processes using event logs
are termed “pro-cess mining” techniques. Their objective is to aid
business analysts inimproving business processes by learning
knowledge from massive data.To date, techniques for process mining
abound. For example, one canmeasure processing time and waiting
time, diagnose process delays andquality issues, and replay an
entire event log over a process model dis-covered from the log
itself. However, these techniques often suffer fromlimited
applicability, particularly when used on top of unpredictable
pro-cesses such as patient treatment processes in healthcare as
opposed topredictable processes such as a car manufacturing
process. They failedto extract a highly fit process model, awkward
in measuring process per-formance, and inaccurate in predictive
monitoring. In addition, they areconfused at how to divide the
problem into sub-problems for better so-lutions. This research aims
at designing a novel set of techniques basedon a notion of business
process stages which can improve over existingprocess mining
techniques.
Keywords: Business process management, process mining,
multistage,stage-based, decomposition
1 Research Motivation
Process Mining [1] was initiated from the field of Business
Process Managementthat oversees and improves human work in
organizations [2]. Therefore, ProcessMining also concerns with
common tasks in BPM such as process performanceanalysis,
conformance checking and root cause analysis. However, differing
fromthe social science branch of BPM concerning interviews,
workshops and surveysfor data collection, Process Mining focuses on
analysing large and rich businessprocess data (called event logs)
available in enterprise IT systems in order toextract useful
knowledge [3]. Process Mining thus is a bridge between BPM anddata
mining.
Like data mining, process mining techniques exploit data
features (or vari-ables) in event logs to learn useful knowledge
for process improvement. Thesetechniques fall into a number of
categories. Process discovery [4] is to deriveprocess models from
event logs. Conformance checking [5] is to align an eventlog with a
process model to verify whether the process execution complies
withthe process design. Performance analysis [6] is to measure
process performance
JolitaTypewritten TextX. Franch, J. Ralyté, R. Matulevičius, C.
Salinesi, and R. Wieringa (Eds.):CAiSE 2017 Forum and Doctoral
Consortium Papers, pp. 161-169, 2017.Copyright 2017 for this paper
by its authors. Copying permitted for private and academic
purposes.
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2 H. Nguyen
metrics to identify bottlenecks. Deviance mining [7] is to
derive business rulesfrom event logs that can explain the root
cause of positive or negative deviants.Predictive monitoring [8]
aims at building predictive models that allow one tomake forecasts
of process performance. Finally, comparative analysis [9] is
tocontrast process variants and extract distinguishing behaviors.
From anotherperspective, these techniques fall into two themes:
structural analysis and be-havioral analysis. In structural
analysis, the purpose is to search for a structurefrom event logs
that highly represents the process, e.g. process models, whichcan
help to do performance analysis, conformance checking and serve as
a basisfor process re-engineering. In behavioral analysis, the
purpose is to search for aset of behaviors (e.g. activity patterns)
that are strongly correlated with a tar-get variable, e.g. long
case duration. Behavioral analysis is common in deviancemining and
predictive monitoring based on trained classifiers such as
decisiontrees [8], random forests [10], and neural networks [11].
In addition, some worksalso regard process models as a source of
generalized behaviors for descriptiveanalysis [9].
Thus far, the main challenge to process mining is that many
event logs exhibita highly complex feature space. For example,
real-life event logs can be foundon the Business Process
Intelligence web site from 2011 to 20171. Notably, theyare often
knowledge-intensive processes [12] such as patient treatment,
insuranceclaim handling, IT incident handling, and loan application
assessment. Their fea-ture space often includes, but not limited
to, activities, humans, data payload,process context [13] and
timestamps. Three main challenges of this feature spaceare the
heterogeneity of case context, the decomposition into
sub-processes, andthe variability of data features. Different case
contexts exist because processcases, e.g. customer orders or
patients, are often prioritized based on differenttypes, e.g.
low-value and high-value cases, and processed differently. Mixing
casecontexts therefore can create greater variation in data
features, thus makes itmore difficult for process structural
analysis. Sub-processes often exist and couldbe in sequence, in
parallel or overlap. They are interrelated but fairly indepen-dent.
Ignoring these sub-processes in one analysis might be the cause of
inac-curate models. Moreover, the inherent variability of data
features in businessenvironment is a challenge to frequent feature
mining for business processes.In many cases, it is the combination
of these three challenges that creates avery heterogeneous feature
space. Consequently, the current problems faced byprocess mining
are scalability and accuracy. For example, process discovery
tech-niques struggle with ill-structured processes [14]. Mining
human-readable rulesfrom event logs remains an issue [7]. The error
rate of predictive monitoringremains remarkably high [15, 11].
Various process mining techniques have been proposed to deal
with the abovecomplex feature space. A common approach is based on
decomposition of eventlogs into clusters, thus able to work with
clusters (i.e. a higher abstraction level)instead of individual
events. It is also known as divide and conquer approachwhich has
been implemented for process discovery [16–20], conformance
check-ing [19], performance analysis [21], deviance mining [22],
and predictive moni-toring [23]. Decomposition can be horizontal
(i.e. by cases) or vertical (i.e. byactivities). However, although
scalability has been improved, the accuracy issueremains [18, 19,
23, 11]. Proposed techniques seem to be ad hoc while they onlywork
with some specific datasets and struggle with others. There are
severalreasons learned from empirical results. For structural
analysis, the proposed de-compositions may underrepresent the real
process structure [24]. Thus, when the
1 www.win.tue.nl/bpi/doku.php?id=2017:challenge
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Stage-based Business Process Mining 3
models are tested against the logs, the result has low fitness
and precision [6].For behavioral analysis, despite the use of
decomposition and strong classifiers,the accuracy could be affected
due to the limited coverage of the process featurespace, e.g. when
a classification model contains only control-flow features butmany
process cases are driven by resources and context [11].
From the above background, this research proposes a novel
process miningapproach based on a notion of business process
stages. Semantically, stages area common way that humans use to
divide their work into manageable parts. Astage thus is also a
sub-process. For example, an outpatient treatment processinvolves
stages such as reception, diagnosis, medication, and consultation.
Stageshave also been observed in BPM research and real datasets,
including patienttreatment[9], IT service delivery[25], government
agency processes [26], bankloan application [27], and product
development [28]. Traditionally, process stageshave been studied in
different disciplines. For example, in manufacturing it isknown
through the state space model for fault diagnosis [29, 30]. In
patient flowresearch, it is called compartment model [31–33]. In
product development, itis known as the stage-gate model [34].
Recently, stage-based analysis has beenstudied in process mining
for inter-organizational comparative analysis but onlyon a manual
basis [9, 35]. Continuing this stream, this research aims to
developstage-based techniques for knowledge-intensive processes
taking advantage ofevent logs and foundational techniques of
process mining.
The intuition here is that stages can help to improve process
mining tech-niques. Intuitively, data features within the same
stages tend to exhibit strongerrelationship than those from
different stages; thus, stage-based techniques couldproduce better
result than those applied to the whole process. For example,stages
could provide a vertical decomposition of event logs (i.e. by
stages) in or-der to improve the quality of process models. The
first question is how to discoverstages from event logs that mimic
the actual stage decomposition. Once stageshave been correctly
discovered, they can be used to discover process models bystages
instead of one flat model for the whole log. Another application of
stagesis to measure flow performance [36]. This kind of performance
is of particularinterest in service organizations such as
hospitals, product development and ITservices because they are
concerned with how smooth cases are pulled throughthe
organizations. Since a stage decomposition consists of adjacent
stages, eachis a fairly independent queueing system, it is thus
allowed to measure flow ofcases (i.e. queuing items) based on
queuing measures computed from event logs,e.g. arrival rate,
departure rate, and length of queue. In addition, in
predictivemonitoring, it could be more accurate to build
classifiers within a stage to pro-vide prediction within that stage
only, combined with inter-stage classifiers toprovide a final
prediction.
2 Research Problems & Research Questions
The previous section has discussed current research problems in
detail. They aresummarized as follows.
1. Current process discovery techniques suffer from low accuracy
for ill-structured processes
2. Current process performance analysis techniques are limited
in measuringthe flow performance of business processes
3. Current predictive process monitoring techniques suffer from
high error ratefor ill-structured processes
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4 H. Nguyen
Our research will be structured to address the following
research questions:
1. How to discover business process stages from event logs?2.
How to mine business process performance from event logs based on
stages3. How to discover process models from event logs based on
stages?4. How to perform predictive process monitoring in
stages?
3 Research Approach
This research project aims at developing stage-based techniques
that can producebetter result than existing techniques. We consider
Design Science (DS) [37]as a relevant research method as its nature
is to produce knowledge based onthe development of artifacts (e.g.
models, frameworks, and methods) to solvea problem [37]). In our
research, the problems would be the research problemsand the
artifacts would be computer software that implements our
proposedtechniques.
Following the Design Science method, this project will primarily
undergo fivemain steps to develop a technique [37]: (i) Define the
problem; (ii) Suggest a so-lution; (iii) Develop artifacts; (iv)
Evaluate the artifacts; (v) Conclude. Amongthese, the validity of
DS-based research is mainly determined by the evaluationof the
artifacts [38]. There are different validation approaches including
obser-vational, analytical, experimental, testing and descriptive
[39]. This project willmainly take the experimental approach given
the data-driven nature of the re-search.
A rigorous approach to experimental evaluation thus is vital to
this project.The evaluations will generally consist of two parts:
data-based and user-based.The former makes use of objective and
quantitative measures while the latterinvolves humans, where
needed, in qualitative assessment. Outline of researchexperiments
are given below.
– Experiments will be carried out on event logs of varied
characteristics– Evaluation will be performed based on
well-established criteria in Data Min-
ing and Process Mining– Controlled experimentation [40] will be
conducted with stakeholders, where
needed, to evaluate the subjective aspect of the research
criteria– The proposed technique will be benchmarked against
baselines available in
the literature
In regards to data collection and analysis, event logs are the
main datasetsused for experiments in this research. Access to data
in different ways is plannedas follows:
– Synthetic datasets will be created for the first validation
using business pro-cess simulation software, e.g. BIMP2 and
CPN-Tools3.
– Real-life datasets will be sourced from repositories of
publicly available logsand industrial as well as academic partners.
The publicly available logs areprovided on academic public data
repositories such as 3TU.Datacentrum4 ofEindhoven University of
Technology which have been used as benchmarkingdata for experiments
in previous research in Process Mining.
2 bimp.cs.ut.ee3 www.cpntools.org4
data.3tu.nl/repository/collection:event logs
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Stage-based Business Process Mining 5
– The student may request for access to datasets of other
research projectswithin the BPM Discipline. The request will be in
compliance with the EthicsClearance of the projects.
– The student will contact the pool of industrial partners of
the BPM Disci-pline such as Commonwealth Bank of Australia, Suncorp
and St AndrewsWar Memorial Hospital, to access further real-life
logs, should this be needed.
4 Preliminary Results
The research thus far has carried out towards addressing the
first two researchquestions: mining process stages from event logs
and mining process performancebased on stages. The result is
reported in the following sections.
4.1 Mining Business Process Stages from Event Logs
Process mining techniques suffer from scalability issues when
applied to largeevent logs, both in terms of computational
requirements and in terms of in-terpretability of the produced
outputs. For example, process models discoveredfrom large event
logs are often spaghetti-like and provide limited insights
[14].
A common approach to tackle this limitation is to decompose the
process intostages, such that each stage can be mined separately.
This idea has been success-fully applied in the context of
automated process discovery [24] and performancemining [41]. The
question is then how to identify a suitable set of stages andhow to
map the events in the log into stages. For simpler processes, the
stagedecomposition can be manually identified, but for complex
processes, automatedsupport for stage identification is required.
Accordingly, several automated ap-proaches to stage decomposition
have been proposed [18, 19, 42]. However, theseapproaches have not
been designed with the goal of approximating manual
de-compositions, and as we show in this work, the decompositions
they produceturn out to be far apart from the corresponding manual
decompositions.
This paper puts forward an automated technique to split an event
log intostages, in a way that mimics manual stage decompositions.
The proposed tech-nique is designed based on two key observations:
(i) that stages are intuitivelyfragments of the process in-between
two milestone events; and (ii) that the stagedecomposition is
modular, meaning that there is a high number of direct
de-pendencies inside each stage (high cohesion), and a low number
of dependen-cies across stages (low coupling) – an observation that
has also been applied inthe context of process model decomposition
[43] and more broadly in the fieldsof systems design and
programming in general. For example, a loan origina-tion process at
a bank has multiple stages such as the application is
assessed(accepted/rejected milestone), offered (offer letter sent
milestone), negotiated(agreement signed milestone), and settled
(agreement executed milestone). Theremay be many back-and-forth or
jumps inside a stage, but relatively little acrossthese stages.
The proposed technique starts by constructing a graph of direct
control-flowdependencies from the event log. Candidate milestones
are then identifiedby using techniques for computing graph cuts. A
subset of these potentialcut points is finally selected in a way
that maximizes the modularity ofthe resulting stage decomposition
according to a modularity measure bor-rowed from the field of
social network analysis. The technique has beenevaluated using
real-life logs in terms of its ability to approximate manual
de-compositions using a well-accepted measure for the assessment of
cluster quality.
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6 H. Nguyen
4.2 Mining Process Performance Based on Staged Process Flows
Process Performance Mining (PPM) is a subset of process mining
techniquesconcerned with the analysis of processes with respect to
performance dimen-sions, chiefly time (how fast a process is
executed); cost (how much a processexecution costs); quality (how
well the process meets customer requirements andexpectations); and
flexibility (how rapidly can a process adjust to changes in
theenvironment) [2].
Along the time and flexibility dimensions, one recurrent
analysis task is tounderstand how the temporal performance of a
process evolves over a given pe-riod of time – also known as flow
performance analysis in lean management [44].For example, a bank
manager may wish to know how the waiting times in aloan application
process have evolved over the past month in order to adjust
theresource allocation policies so as to minimize the effects of
bottlenecks.
Existing PPM techniques are not designed to address such flow
performancequestions. Instead, these techniques focus on analyzing
process performance ina “snapshot” manner, by taking as input an
event log recorded during a periodof time and extracting aggregate
measures such as mean waiting time, process-ing time or cycle time
of the process and its activities. For example, both thePerformance
Analysis plugins of ProM [45] and Disco [46] calculate
aggregateperformance measures (e.g. mean waiting time) over the
entire period coveredby an event log and display these measures by
color-coding the elements of aprocess model. These tools can also
produce animations of the flow of casesalong a process model over
time. However, extracting flow performance insightsfrom these
animations requires close and continuous attention from the
analystin order to detect visual cues of performance trends,
bottleneck formation anddissolution, and phase transitions in the
process performance. In other words,animation techniques allow
analysts to get a broad picture of performance issues,but not to
precisely quantify the evolution of process performance over
time.
In this setting, this paper presents a PPM approach designed to
provide aprecise and quantifiable picture of flow performance. The
approach relies on anabstraction of business processes called
Staged Process Flow (SPF). An SPFbreaks down a process into a
series of queues corresponding to user-definedstages. Each stage is
associated with a number of performance characteristicsthat are
computed at each time point in an observation window. The
evolutionof these characteristics is then plotted via several
visualization techniques thatcollectively allow flow performance to
be analyzed from multiple perspectives inorder to address the
following questions:
Q1. How does the overall process performance evolve over
time?Q2. How does the formation and dissolution of bottlenecks
affect the overall
process performance?Q3. How do changes in demand and capacity
affect the overall process perfor-
mance?
The paper demonstrates the advantages of the SPF approach over
state-of-the-art process performance mining tools using real-life
event logs of a Dutchbank and IT department of Volvo Belgium.
5 Conclusion and Future Work
This paper describes an overall approach of stage-based process
mining basedon observed gaps in current process mining techniques.
So far, we have proposed
166
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Stage-based Business Process Mining 7
two stage-based techniques: one for discovering business process
stages fromevent logs and one for mining process flow performance
from event logs basedon stages. The former work shows that our
stage decomposition technique canprovide results that are
measurably much closer to the ground truth than thebaselines. The
latter work shows that it provides insights and addresses
questionsthat cannot be answered by existing performance mining
techniques. In thefuture, we will continue developing stage-based
techniques for process discoveryand predictive process
monitoring.
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