=Paper=
{{Paper
|id=Vol-1789/bpm-demo-2016-paper7
|storemode=property
|title=Behavior-based Process Comparison in Apromore
|pdfUrl=https://ceur-ws.org/Vol-1789/bpm-demo-2016-paper7.pdf
|volume=Vol-1789
|authors=Abel Armas-Cervantes,Nick R.T.P. van Beest,Marlon Dumas,Luciano García-Bañuelos,Marcello La Rosa
|dblpUrl=https://dblp.org/rec/conf/bpm/Armas-Cervantes16
}}
==Behavior-based Process Comparison in Apromore==
https://ceur-ws.org/Vol-1789/bpm-demo-2016-paper7.pdf
Behavior-Based Process Comparison in Apromore
Abel Armas-Cervantes1 , Nick R.T.P. van Beest2 , Marlon Dumas3 , Luciano
Garcı́a-Bañuelos3 , and Marcello La Rosa1
1 Queensland University of Technology, Australia
{abel.armascervantes,m.larosa}@qut.edu.au
2 Data61, CSIRO, Australia
nick.vanbeest@data61.csiro.au
3 University of Tartu, Estonia
{marlon.dumas,luciano.garcia}@ut.ee
Abstract. This paper presents the integration of three behavior-based compar-
ison operations between logs and/or models into the Apromore process model
repository. Each of these operations takes as input a pair of process artifacts (two
event logs, two models, or one log and one model) and describes their behav-
ioral differences by means of natural language statements. The generated differ-
ence diagnosis has a range of applications of interest to both practitioners and
researchers. For example, the difference diagnosis can offer guidance for recon-
ciling discrepancies between business process variants (model2model compari-
son); can be used to pinpoint and explain differences between actual and expected
behavior for conformance checking purposes (model2log comparison); or can ex-
plain dissimilarities between normal and deviant executions of a process (log2log
comparison).
Keywords: Apromore, behavioral comparison, deviance mining, conformance check-
ing, consolidation of variants
1 Overview
The behavioral comparison of process models and event logs is a recurrent primitive
in business process analysis. Conformance checking, deviance mining and (behavior-
based) process model comparison can be seen as specific instances of this general prim-
itive. In particular, conformance checking aims at finding if the observed behavior cap-
tured in an event log complies with the behavior specified in the model; deviance mining
compares the behavior captured in event logs to explain why a business process deviates
from its normal or expected behavior; finally, (behavior-based) process model compar-
ison aims at explaining the behavioral differences between process models that may
correspond to variants of the same business process.
A critical feature of behavioral comparison operations is the interpretability of the
identified differences between logs, models or between a model and a log. In this re-
gard, a set of techniques for conformance checking, deviance mining and process model
Copyright c 2016 for this paper by its authors. Copying permitted for private and academic
purposes.
Behavior-Based Process Comparison in Apromore 35
comparison have been proposed in [1, 2], [3] and [4, 5], respectively. These techniques
use event structures [6], a well-known model of concurrency, as unified behavioral ab-
stractions for both logs and models [7]. This comparison approach is independent of
the input-format, i.e. process models, event logs or both. The differences are reported
to the user in the same way regardless of the input type, using a set of sentences in
natural language, without requiring in-depth knowledge of formal modeling languages
like Petri nets. Consequently, our tool is suitable for use by business analysts and, as
opposed to other approaches, it does not require process modeling or mining experts to
interpret the results. Figure 1 depicts an overview of the implemented operations.
Model 1 Model 2
Model to model
comparison
(e.g., consolidation
Prime event of variants) Prime event
structure of Compare structure of
Model 1 Model 2
Model to log
comparison
Compare Compare
(e.g., conformance
checking)
Prime event Prime event
structure of Compare structure of
Log 1 Log to log Log 2
comparison
(e.g., deviance
mining)
Log 1 Log 2
Fig. 1: Overview of the implemented comparison operations.
This paper presents the integration of the techniques proposed in [1, 2], [3] and [4, 5]
into the Apromore process model repository 2 . Apromore is an open-source and exten-
sible online repository of state-of-the-art capabilities for managing large process model
collections. The operations for conformance checking, deviance mining and (behavior-
based) process model comparison complement the wide range of existing capabilities,
e.g., process model merging, simulation, restructuring, querying and similarity search,
provided by Apromore. The three techniques are wrapped into a pairwise Compare op-
eration that will automatically execute the appropriate comparison operation depending
on the type of selected artifacts as input, two models, one model and one log, two logs.
The modeling languages supported are all those available in Apromore, namely BPMN,
EPCs, Petri nets and YAWL. The log format can be XES or MXML.
2 http://apromore.qut.edu.au
36 Armas-Cervantes et al.
(a)
(b) (c)
(d)
Fig. 2: Apromore Compare interface.
Given a pair of process artifacts, the steps for the three types of comparison are:
1. computation of event structures out of event logs and/or models, 2. comparison of
event structures, and 3. verbalization of the identified differences. In the current imple-
mentation the type of event structures used is prime event structures [6], the comparison
is performed using a so called partial synchronized product [4], and the verbalization
of the differences is aligned with the corresponding papers [1, 3–5].
Figure 2 shows the different user interfaces in Apromore for the comparison of two
logs and two models. Figure 2(a) shows the menu in Apromore where the Compare
Behavior-Based Process Comparison in Apromore 37
operation is located, Figure 2(b) depicts the input dialog to upload a pair of event logs
and Figure 2(c) shows the popup window displaying the statements in natural language
explaining the identified differences. Finally, Figure 2(d) depicts the representation of
the differences resulting from the comparison of two models. In this case, the differ-
ences are represented in two ways: 1. as statements in natural language (left) and 2. as
overlaid graphics on the process models (right).
2 Significance and Maturity
Our toolchain exhibits a novel approach for identifying root causes of deviant process
executions (via event log comparison), identifying differences between process execu-
tions and normative process specifications (conformance checking of an event log with
a process model), and identifying differences between different versions or specifica-
tions of process models (i.e. comparing two different business process models).
The approach can be applied both in intra-organizational and cross-organizational
settings. For instance, different process variants and executions in public organizations
can be identified and analyzed to obtain a set of generic models (e.g. including best
practices) and a set of additional organization-specific features. In addition, process
executions in different organizational branches can be analyzed to identify causes for
performance differences.
This approach provides for the first time a tool that abstracts from the representation
(i.e. process model or event log) and is capable of reporting a complete set of differ-
ences via natural language statements. The set of produced statements are compact and
interpretable to allow for a clear overview of differences between process models and/or
logs of process executions. The reporting of differences is specifically intended to in-
form business analysts, and comparison of complex models and logs is, as such, no
longer limited to technical users only. Consequently, users who are directly involved
in the business process under investigation are now able to interpret the results of the
comparison. Furthermore, the results provided are complete (i.e. all behavioral differ-
ences are identified), more compact and precise than existing approaches and provide,
therefore, a much better assessment of differences between process models and logs.
The Apromore features used in this approach have been evaluated extensively with
respect to accuracy, scalability and advantages over existing approaches. The evalu-
ations comprised large collections of both artificial and real-life process models and
event logs. The qualitative evaluation showed that the presented toolchain produces
a more compact and much more understandable diagnosis than existing techniques.
Furthermore, the tool exposes differences that are difficult or impossible to identify
otherwise. The quantitative evaluation involved over 700 real-life process models and
showed that the proposed approach has reasonable execution times (within seconds).
Even in extreme cases with a high number of differences between the process model
and the event log (with the event log containing more than 8,000 event occurrences,
considering distinct traces only), the execution time is still within a few minutes. The
detailed results of these evaluations are reported in [3] (log2log), [1] (model2log) and
[4, 5] (model2model).
38 Armas-Cervantes et al.
Apromore features an OSGi plugin framework to support dynamic enabling/dis-
abling of plugin bundles and multiple bundle versions. These capabilities include pre-
sentation capabilities with respect to process model restructuring, filtering of models
based on process-related aspects, searching and querying for specific process patterns,
advanced design of process models, including configuring and merging if existing mod-
els, and evaluation capabilities to assess the quality and correctness of models, as well
as simulation and conformance checking techniques for benchmarking. Furthermore,
Apromore provides full import and export functionality to a large variety of business
process modeling languages and log formats, such as BPMN, XPDL, EPML, ARIS,
YAWL, PNML, XES and MXML.
Apromore is the result of over six years of ongoing development and is currently
in version 3.4. The platform is implemented via a service-oriented architecture and de-
ployed as a Software as a Service. The technologies used in Apromore combine Spring
as the Java development framework, Maven as the dependency manager, OSGi as the
plugin architecture, EclipseVirgo as the OSGi-based application server, and ZK as the
AJAX front end. The chosen technologies allow Apromore to be an extensible frame-
work, where new plugins can be easily added to an ecosystem of advanced capabilities
for managing process model collections.
3 Screencast
A screencast of Apromore’s compare feature can be found at http://goo.gl/JB1EDv.
The public release of Apromore is available at http://apromore.qut.edu.au and
its source code can be downloaded under the GNU LGPL license version 3.0 from
https://github.com/apromore/ApromoreCode.
References
1. Garcı́a-Bañuelos, L., van Beest, N.R.T.P., Dumas, M., La Rosa, M.: Complete and inter-
pretable conformance checking of business processes. QUT ePrints, Technical report #91552,
http://eprints.qut.edu.au/91552/ (December 2015)
2. Garcı́a-Bañuelos, L., van Beest, N.R.T.P., Dumas, M., La Rosa, M.: Business process confor-
mance checking based on event structures. In: Proc. of NWPT’2015, Reykjavik University
(2015) 3 pages.
3. van Beest, N.R.T.P., Dumas, M., Garcı́a-Bañuelos, L., La Rosa, M.: Log delta analysis: Inter-
pretable differencing of business process event logs. In: Proc. of BPM 2015, Springer (2015)
386–405
4. Armas-Cervantes, A., Baldan, P., Dumas, M., Garcı́a-Bañuelos, L.: Diagnosing behavioral
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5. Armas, A., Baldan, P., Dumas, M., Garcı́a-Bañuelos, L.: Behavioral comparison of process
models based on canonically reduced event structures. In: BPM 2014, Springer (2014) 267–
282
6. Nielsen, M., Plotkin, G.D., Winskel, G.: Petri Nets, Event Structures and Domains, Part I.
TCS 13 (1981) 85–108
7. Dumas, M., Garcı́a-Bañuelos, L. In: Process Mining Reloaded: Event Structures as a Unified
Representation of Process Models and Event Logs. Springer (2015) 33–48