=Paper=
{{Paper
|id=Vol-1295/paper21
|storemode=property
|title=Discovering, Analyzing and Enhancing BPMN Models Using ProM
|pdfUrl=https://ceur-ws.org/Vol-1295/paper21.pdf
|volume=Vol-1295
|dblpUrl=https://dblp.org/rec/conf/bpm/KalenkovaLA14
}}
==Discovering, Analyzing and Enhancing BPMN Models Using ProM==
https://ceur-ws.org/Vol-1295/paper21.pdf
Discovering, Analyzing and Enhancing BPMN Models
Using ProM?
Anna A. Kalenkova1 , Massimiliano de Leoni2 , and Wil M.P. van der Aalst2,1
1
National Research University Higher School of Economics, Moscow, 101000, Russia
akalenkova@hse.ru
2
Eindhoven University of Technology, Eindhoven, The Netherlands
{w.m.p.v.d.aalst,m.d.leoni}@tue.nl
Abstract. Process mining techniques relate observed behavior to modeled be-
havior, e.g., the automatic discovery of a process model based on an event log.
Process mining is not limited to process discovery and also includes conformance
checking and model enhancement. Conformance checking techniques are used to
diagnose the deviations of the observed behavior as recorded in the event log from
some process model. Model enhancement allows to extend process models us-
ing additional perspectives, conformance and performance information. In recent
years, BPMN (Business Process Model and Notation) 2.0 has become a de facto
standard for modeling business processes in industry. This paper presents the
BPMN support current in ProM. ProM is the most known and used open-source
process mining framework. ProM’s functionalities of discovering, analyzing and
enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will
help ProM users to bridge the gap between formal models (such as Petri nets,
causal nets and others) and process models used by practitioners.
1 Overview
Process Aware Information Systems (PAIS) are increasingly used by organizations to
support their businesses. All these systems record the execution of process instances
in so-called event logs. These logs thus capture information about activities performed.
Each event records the execution of an activity instance by a given resource at a certain
point in time along with the output produced. Analyzing event logs, understanding and
improving processes based on facts are the primary objectives of process mining [9].
In this relatively short timespan, this discipline has proven to be capable of extracting
from event logs in-depth insights into process-related problems that contemporary en-
terprises face. Through the application of process mining, organizations can discover
models of the processes as they were conducted in reality, check whether the actual ex-
ecutions comply with a prescribed process model, which encode certain practices and
regulations. Furthermore, process mining allows analysts to gain insight into bottle-
necks, resource utilization, and other performance-related aspects of processes.
?
This work is supported by the Basic Research Program of the National Research University
Higher School of Economics. When conducting this work, Dr. de Leoni was also affiliated with
University of Padua, Italy and financially supported by the Eurostars-Eureka project PROMPT
(E!6696)
Copyright c 2014 for this paper by its authors. Copying permitted for private and academic purposes.
2
Most of the process-mining techniques usually need a process model as an input
or it is produced as an output. The academic world has proposed many process-model
notations in the last years, such as Petri nets, causal nets [4] and process trees [5].
Nonetheless, the evidence is showing that, during the last years, BPMN (Business Pro-
cess Model and Notation) 2.0 [2] is becoming the de-facto standard for modeling busi-
ness processes in the industry. Therefore, it seems crucial that every process-mining
technique is able to deal with BPMN models. Unfortunately, these techniques have
been devised using different notations, which are often equivalent. To overcome this
problem, two solutions are possible: either these techniques are adapted to use a BPMN
model as input or produce one as output, or robust conversion mechanisms are provided
to convert process models from these alternative notations to the BPMN notation, and
vice versa.
This paper starts from the belief that the second solution is the most feasible. In this
paper, we discuss the operationalization of various techniques to convert models from
certain notations to BPMN. These conversions techniques have been implemented as
plugins for ProM [3], a generic open-source framework for implementing process min-
ing tools in a standard environment. Several companies and universities around the
globe have contributed to the ProM development, designing and implementing plugins
to test their own process mining algorithms. The ProM framework is based on the con-
cept of packages each of which is an aggregation of several plugins that are conceptually
related. In the latest version, 6.3, there are already more than 120 packages containing
more than 500 plugins available, operationalizing techniques in the entire spectrum of
process mining. By implementing our conversion techniques in ProM, many of these
plugins can now produce BPMN models. Furthermore, we extended ProM to be able
to load and store BPMN models using standard formats (see later). In this way, the
discovered models can be loaded into an external BPMN modeling tool or into a work-
flow engine that supports the execution of BPMN models. Similarly, the BPMN models
drawn in external tools can be loaded in ProM and used as an input for the diverse
process mining analysis.
Last but not least, we have developed a technique to enhance a BPMN model us-
ing performance and conformance information. To analyze a process represented as a
BPMN model first the model has to be converted to a corresponding Petri net or other
formal model. After that this model is verified against the log, retrieving performance
characteristics (activities working times, frequencies and probabilities of occurrence in
a trace) and conformance information (deviations between the process model and the
log).
We support a large subset of the entire BPMN notation; in the addition of supporting
the control-flow constructs (activities, connecting arcs and gateways), we also allow for
the different types of data objects, swimlanes, subprocesses and events. The structure
of the BPMN-related packages in ProM is depicted in Fig. 1. The core BPMN package
operates BPMN models and gives an ability to import and export BPMN diagrams in
BPMN XML 2.0 [2] and XPDL 2.2 [1] formats. Other BPMN packages depend on this
package. The BPMN Conversions package allows to construct BPMN-process models
from well-known control flow modeling formalisms such as Petri nets, causal nets [4]
and process trees [5].
3
Thus, using BPMN Conversions pack-
age, BPMN processes can be discovered. The ProM
BPMN to Petri net conversion was imple- Replay
mented as well [6] 1 , this conversion can be «call» plugins
used to analyze BPMN diagrams. Moreover,
the BPMN Conversions package provides the «call»
ability to enhance BPMN diagrams with data BPMN BPMN
Analysis Conversions
and resource perspectives: data Petri nets dis- package package
covered using the data-aware process mining
algorithm [8] can be converted to BPMN di- «import» «import»
agrams capturing both the control and data BPMN
package
perspectives (including data objects and gate-
way guards), process trees can be converted
Import/export of BPMN
to BPMN along with the resource nodes, diagrams in BPMN XML 2.0
translating them to BPMN lanes. The BPMN and XPDL 2.2 formats
Analysis package in its turn enhances BPMN
BPMN modeling tool
diagram by adding performance and confor-
mance (log and model discrepancies) infor-
mation. The core BPMN package supports
all main BPMN elements and has been con- Fig. 1: BPMN packages architecture
tinuously extended. The BPMN Conversion
package offers fully implemented plugins for conversion between BPMN and formal
process models. The functionality of the BPMN Analysis package is still being im-
proved and tested. All the plugins tailored towards working with BPMN in ProM are
presented in Table 1.
Package name Plugin name Functionality
Core BPMN package BPMN Import/Export Implements import and export of BPMN diagrams
plugins in BPMN XML 2.0 and XPDL 2.2 formats
BPMN Conversions Convert Petri net Converts a given Petri net to a BPMN model
BPMN Conversions Convert Data Petri net Converts a data Petri net to a BPMN model with
data perspective
BPMN Conversions Convert causal net Converts a causal net to a BPMN model
BPMN Conversions Convert process tree Converts a process tree along with resource nodes
to a BPMN model with a resource perspective
BPMN Conversions Convert BPMN model to Converts a BPMN model to a corresponding Petri
Petri net 1 net
BPMN Analysis Analyze BPMN model Enhances a BPMN model using performance and
conformance information
Table 1: The list of BPMN plugins in ProM
2 Use cases
In this section we discuss usage scenarios of BPMN-related functionality of ProM.
The scheme of usage of BPMN plugins in ProM is presented in Fig. 2. The user can
1
A special thanks to Dirk Fahland, who has implemented the BPMN to Petri net conversion
algorithm in ProM.
4
discover a BPMN model applying discovery and BPMN conversions plugins, after
that this model can be annotated with conformance and performance information.
Let us consider an example of construct- Event log
ing a BPMN process model from an event
Process discovery algorithms
log. Suppose that we have discovered a data
Petri net using data-aware process mining al- Petri net Process tree
(with data) Causal net
(with resources)
gorithm [8] (Fig. 3 a.). A BPMN process
BPMN Conversions package
model constructed from the data Petri net is Conversions to BPMN
presented in Fig. 3 b. This BPMN model can
be exported to an external BPMN modeling BPMN
tool such as Signavio [7] (Fig. 3 c.). This ex- BPMN to Petri net
conversion
ample illustrates that the process discovered Petri net
from an event log can be finally represented
Evaluation of performance
as a BPMN diagram with data and gateway and conformance info
guards and loaded to an external BPMN tool Performance and
conformance info
for further analysis or even execution. BPMN Analysis package
The other possible usage is that the user
Add performance and conformance info
imports a BPMN model from an external
BPMN modeling tool (or discovers a BPMN Annotated
BPMN
model using discovery and conversion plug-
ins), applies replay technique to retrieve per- Fig. 2: Functionality of BPMN Conver-
formance and conformance information and sions and BPMN Analysis packages
annotate the BPMN diagram using this infor-
mation. Figure 4 a. shows a BPMN process
model created in the Signavio tool, this model is loaded to ProM (Figure 4 b.), and then
analysis techniques are applied: performance and conformance information for the en-
tire process model and each activity in particular are added to the diagram (Figure 4 c.).
Data Petri net to
a. BPMN Conversion
c. b.
Import to Signavio
Fig. 3: Discovering a BPMN model with data
5
a. b.
Export from Signavio
c.
Replaying
Addition of
conformance and
performance info
Fig. 4: Analysis of a BPMN model
The entire support for BPMN discussed in this paper is available in the nightly build of
ProM and is mature enough to be applied to real business cases. Readers can learn how
to install and try out the BPMN support for ProM at http://pais.hse.ru/en/
research/projects/HLM. At the same link, a screencast video and a presentation
are available, showing the application of the two use cases discussed in Section 2.
References
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