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https://ceur-ws.org/Vol-1371/frontmatter.pdf
Workshop Proceedings
Workshop on
Algorithms & Theories for the
Analysis of Event Data (ATAED’2015)
Brussels, Belgium, June 22-23, 2015
Satellite event of the conferences
15th International Conference on Application of
Concurrency to System Design (ACSD 2015)
36th International Conference on Application and Theory
of Petri Nets and Concurrency (PN 2015)
Edited by
Wil van der Aalst, Robin Bergenthum, and Josep Carmona
.
These proceedings are published online by the editors as Volume 1371 at CEUR
Workshop Proceedings
http://ceur-ws.org/Vol-1371
Copyright c 2015 for the individual papers is held by the papers authors. Copy-
ing is permitted only for private and academic purposes. This volume is published
and copyrighted by its editors.
Preface
Regions have been defined about 20 years ago by Ehrenfeucht and Rozen-
berg as sets of nodes of a finite transition system that correspond to potential
conditions that enable or disable transition occurrences in a corresponding ele-
mentary net system. Later, similar concepts were used to derive Petri nets from
languages. Both state-based and language-based approaches aim to constrain a
Petri net by adding places that are called regions. Over time, many variations
have been proposed, e.g., approaches dealing with multiple-token in a place,
extensions to partial orders, etc.
Initially, region theory focused on synthesis where the input behavior and
resulting Petri net are supposed to be equivalent with respect to some equivalence
criterion (e.g., bisimilar). Recently, region-based research started to focus also on
process mining where the goal is not to create an equivalent model, but to infer
new knowledge from the input. Process mining takes as input observed behavior
rather than assuming a complete description in terms of a transition system or
prefix-closed language. Classical region based techniques are unable to discover
process models from event logs. One needs to deal with new problems such as
noise and incompleteness. Equivalence notions are replaced by trade-offs between
fitness, simplicity, precision, and generalization. A model with good fitness allows
for most of the behavior seen in the event log. A model that does not generalize
is “overfitting”. Overfitting is the problem that a very specific model is generated
whereas it is obvious that the log only holds example behavior. A model that
allows for “too much behavior” lacks precision (i.e., is underfitting). Simplicity
is related to Occam’s Razor which states that “one should not increase, beyond
what is necessary, the number of entities required to explain anything”. Following
this principle, we often look for the simplest process model that can explain
what was observed in the event log. Process discovery from event logs is very
challenging because of these and many other trade-offs. Clearly, there are many
theoretical process-mining challenges with a high practical relevance that need
to be addressed urgently.
The challenges and opportunities formed the main motivation for propos-
ing the Algorithms & Theories for the Analysis of Event Data (ATAED’2015)
workshop as a succession of the Applications of Region Theory (ART) workshop
series. Our goal was (and is) to bring together researchers working on region-
based synthesis and process mining. Looking at the proceedings, we succeeded
in doing so!
The ATAED’2015 workshop took place in Brussels on June 22-23, 2015 and
was a satellite event of both the 36th International Conference on Application
and Theory of Petri Nets and Concurrency (Petri nets 2015) and the 15th In-
ternational Conference on Application of Concurrency to System Design (ACSD
2015). Papers related to process mining, region theory and other synthesis tech-
niques were presented at ATAED’2015. These techniques have in common that
‘lower level’ behavioral descriptions (event logs, partial orders, transition sys-
tems, etc.) are used to create ‘higher level’ process models (e.g., various classes
of Petri nets, BPMN, or UML activity diagrams). In fact, all techniques that
aim at learning or checking concurrent behavior from transition systems, runs,
or event logs were welcomed. The workshop was supported by the IEEE Task
Force on Process Mining (www.win.tue.nl/ieeetfpm/).
After a careful reviewing process, eleven papers were accepted for the work-
shop. Overall, the quality of the submitted papers was good and most submis-
sions matched very well the workshop goals. We thank the reviewers for providing
the authors with valuable and constructive feedback. Moreover, we were honored
that Eike Best (University of Oldenburg) was willing to give an invited talk on
the “Synthesis of Diamonds”. We thank Eike, the authors, and the presenters
for their wonderful contributions.
In the remainder, the accepted papers of the Algorithms & Theories for the
Analysis of Event Data (ATAED’2015) workshop are briefly summarized.
– The paper “On Binary Words Being Petri Net Solvable” by Kamila Barylska,
Eike Best, Evgeny Erofeev, Lukacs Mikulski, and Marcin Piatkowski stud-
ies the class of two-letter Petri net solvable words, i.e., Petri nets with two
transitions and a reachability graph isomorphic to a trace-based transition
system. Several intriguing results are presented for this class, e.g., the exis-
tence of side-place-free solutions given particular conditions.
– Andrey Mokhov and Josep Carmona use Conditional Partial Order Graphs
(CPOGs) to create compact and easy-to-comprehend visualizations of event
logs with data. In their paper “Event Log Visualization with Conditional
Partial Order Graphs: From Control Flow to Data”, the authors provide a
technique to automatically derive the control-flow part of the CPOG repre-
sentation from an event log, and then incorporate the data contained in the
log as conditions for the CPOG vertices and arcs.
– The paper “Discovery of Personal Processes from Labeled Sensor Data: An
Application of Process Mining to Personalized Health Care” by Timo Sztyler,
Johanna Völker, Josep Carmona, Oliver Meier, and Heiner Stuckenschmidt
shows how process mining can be used for analyzing self-tracking data.
Smart-phones and smart-watches can be used to produce detailed data about
someone’s daily life. The authors describe the acquisition of such data in real-
life and use existing process mining techniques for eliciting, analyzing and
monitoring daily routines.
– “ILP-Based Process Discovery Using Hybrid Regions” by Sebastiaan van
Zelst, Boudewijn Van Dongen, and Wil van der Aalst unifies the two exist-
ing types of language-based regions (single variable-based regions and dual
variable-based regions) to provide a representation suitable for process min-
ing. Integer Linear Programming (ILP)-based process discovery is further
enhanced with a generalized ILP objective function. It is shown that any
instantiation of the objective function leads to ILPs that favor minimal re-
gions.
– Robin Bergenthum, Thomas Irgang, and Benjamin Meis present a folding
algorithm to construct a business process model from a specification in their
paper “Folding Example Runs to a Workflow Net”. Different to mainstream
process mining techniques the input is not a sequential event log but a set of
example runs represented as labeled partial orders. By adopting ideas from
the theory of regions, the authors aim at improving precision of the model
while folding the runs into a model.
– The paper “Mining Duplicate Tasks from Discovered Processes” by Borja
Vázquez-Barreiros, Manuel Mucientes, and Manuel Lama tackles the clas-
sical problem of label splitting in process mining. The authors propose an
approach that uses the local information in the log to enhance an already
mined model by performing a local search over the potential tasks to be
duplicated. Experimental results show that, in a case study, the final model
was improved in 35 out of 36 cases.
– The paper “A Method For Assessing Parameter Impact on Control-Flow Dis-
covery Algorithms” by Joel Ribeiro and Josep Carmona presents a method
to automatically assess the impact of parameters of control-flow discovery
algorithms. The metaheuristic approach for process mining can be used to
guide the user in selecting a technique, representational bias, and suitable
parameter setting. The method has been evaluated over a set of logs while
using the flexible heuristic miner.
– Antonia Azzini, Paolo Ceravolo, Ernesto Damiani, and Francesco Zavatartelli
introduce the notion of extended behavior in their paper “Knowledge Driven
Behavioural Analysis in Process Intelligence”. They present a methodology
where first the descriptive knowledge is collected and the knowledge base
queried, then (in the prescriptive and predictive knowledge phases) business
rules and objectives are evaluated and unexpected business patterns and
exceptions are uncovered.
– “Compact Regions for Place/Transition Nets” by Robin Bergenthum presents
an approach using compact regions to synthesize a Petri net from a partial
language. The language of a marked Petri net is its set of compact valid
example runs. Compact regions are relevant as they may lead to faster syn-
thesis algorithms computing smaller Petri nets. Initial results suggest that
synthesis is indeed faster and that the compact solution space leads to nets
having less places.
– In “An Optimal Process Model for a Real Time Process”, the authors Likewin
Thomas, Manoj Kumar M.V., Annappa B., and Vishwanath K.P. provide a
solution for recommending an optimal path of execution taking into account
resource allocations. The proposed AlfyMiner compares variants of the same
process encountered in different organizations. The authors include func-
tionality to compare processes and to analyze resource behavior. This is
then used to recommend next actions and suitable resources.
– The paper “Capturing the Sudden Concept Drift in Process Mining” by
Manoj Kumar M.V., Likewin Thomas and Annappa B. focuses on sudden
changes during process execution, i.e., second-order process dynamics. The
paper proposes the extraction of a so-called “event class correlation feature”
from logs for localizing the sudden concept drift in the control-flow perspec-
tive of the operational process. Experiments using synthetic event data show
that (under ideal circumstances) sudden process changes can be detected.
The workshop proceedings provide a nice selection of ongoing research on the
intersection of process mining and region-based synthesis. The papers illustrate
the range of problems and solution approaches related to lifting ‘lower level’
dynamic behavior to ‘higher level’ process models. Given the rapid growth of
event data, the area is expected to become even more relevant in years to come.
We hope that ATAED’2015 serves as a starting point for a viable workshop
series bringing together the two communities working on process mining and
region-based synthesis.
Enjoy reading the proceedings!
Wil van der Aalst, Robin Bergenthum, and Josep Carmona
June 2015
Program committee of ATAED’2015
Wil van der Aalst, TU Eindhoven, The Netherlands (co-chair)
Rafael Accorsi, Universitaet Freiburg, Germany
Eric Badouel, INRIA Rennes, France
Robin Bergenthum, FernUni Hagen, Germany (co-chair)
Luca Bernardinello, Universit degli studi di Milano-Bicocca, Italy
Seppe vanden Broucke, KU Leuven, Belgium
Benoı̂t Caillaud, INRIA Rennes, France
Toon Calders, Universit Libre de Bruxelles, Belgium
Josep Carmona, UPC Barcelona, Spain (co-chair)
Paolo Ceravolo, University of Milan, Italy
Benoı̂t Depaire, Hasselt University, Belgium
Jörg Desel, FernUni Hagen, Germany
Boudewijn van Dongen, TU Eindhoven, The Netherlands
Luciano Garca-Bañuelos, University of Tartu, Estonia
Luı́s Gomes, Universidade Nova de Lisboa, Portugal
Gabriel Juhás, Slovak University of Technology, Slovak Republic
Anna Kalenkova, Higher School of Economics NRU, Russia
Jetty Kleijn, Leiden University, The Netherlands
Robert Lorenz, Uni Augsburg, Germany
Zbigniew Paszkiewicz, PricewaterhouseCoopers, Belgium
Marta Pietkiewicz-Koutny, Newcastle University, GB
Grzegorz Rozenberg, Leiden University, The Netherlands
Marcos Sepúlveda, Universidad Catolica de Chile, Chile
Jianmin Wang, Tsinghua University, China
Jochen De Weerdt, KU Leuven, Belgium
Alex Yakovlev, Newcastle University, GB
Table of Contents
K. Barylska, E. Best, E. Erofeev, L. Mikulski, M. Piatkowski
On Binary Words Being Petri Net Solvable 1 - 15
A. Mokhov, J. Carmona
Event Log Visualisation with Conditional Partial Order Graphs from
Control Flow to Data 16 - 30
T. Sztyler, J. Völker, J. Carmona, O. Meier, H. Stuckenschmidt
Discovery of Personal Processes from Labeled Sensor Data -
An Application of Process Mining to Personalized Health Care 31 - 46
S.J. van Zelst, B.F. van Dongen, W.M.P. van der Aalst
ILP-Based Process Discovery Using Hybrid Regions 47 - 61
R. Bergenthum, T. Irgang, B. Meis
Folding Example Runs to a Workflow Net 62 - 77
B. Vázquez-Barreiros, M. Mucientes, M. Lama
Mining Duplicate Tasks from Discovered Processes (short paper) 78 - 82
J. Ribeiro, J. Carmona
A Method for Assessing Parameter Impact on Control-Flow
Discovery Algorithms 83 - 96
A. Azzini, P. Ceravolo, E. Damiani, F. Zavatarelli
Knowledge Driven Behavioural Analysis in Process Intelligence 97 - 111
R. Bergenthum
Compact Regions for Place/Transition Nets (short paper) 112 - 116
Likewin Thomas, Manoj Kumar M V, Annappa B, Vishwanath K P
An Optimal Process Model for a Real Time Process 117 - 131
Manoj Kumar M V, Likewin Thomas, Annappa B
Capturing the Sudden Concept Drift in Process Mining 132 - 143