=Paper=
{{Paper
|id=Vol-2196/BPM_2018_paper_15
|storemode=property
|title=OpyenXES: A Complete Python Library for the eXtensible Event Stream Standard
|pdfUrl=https://ceur-ws.org/Vol-2196/BPM_2018_paper_15.pdf
|volume=Vol-2196
|authors=Hernan Valdivieso,Wai Lam Jonathan Lee,Jorge Munoz-Gama,Marcos Sepúlveda
|dblpUrl=https://dblp.org/rec/conf/bpm/ValdiviesoLMS18
}}
==OpyenXES: A Complete Python Library for the eXtensible Event Stream Standard==
https://ceur-ws.org/Vol-2196/BPM_2018_paper_15.pdf
OpyenXES: A Complete Python Library for the
eXtensible Event Stream Standard
Hernan Valdivieso, Wai Lam Jonathan Lee,
Jorge Munoz-Gama, and Marcos Sepúlveda
Computer Science Department, School of Engineering
Pontificia Universidad Católica de Chile, Santiago, Chile
{hfvaldivieso,walee,jmun}@uc.cl
{marcos}@ing.puc.cl
Abstract. There has been a spectacular growth in the availability of
event data. To transport, store and exchange these event data, the eXten-
sible Event Stream (XES) has become the acknowledged standardization
due to its simplicity, flexibility, extensibility and expressivity. Currently,
the OpenXES library exists as the popular open-source Java implemen-
tation of the XES standard. However, despite the gaining popularity of
Python as the core programming language for data science, there has yet
to exist a complete and open-source implementation of the standard in
the language. This paper presents OpyenXES as a complete and open-
source implementation of the XES standard in Python. This opens up
the rich portfolio of Python packages for data science to researchers and
practitioners in the field of business process management.
Keywords: event log, XES, Python, open-source, data science
1 Significance to the BPM Field
Nowadays we live in the Big Data era. Massive amounts of event data about our
daily processes are recorded by information systems and electronic devices alike
in the so-called Internet of Events [4]. For process stakeholders, one main goal
is to turn event data into real value, e.g., improving the efficiency of the process.
Under this context, a new format for event data has appeared: the eXtensible
Event Stream (XES) [2]. XES is an XML-based standard for event logs and its
purpose is to provide a generally-acknowledged format for the interchange of
event log data between tools and application domains [2]. In 2016, the IEEE
approved XES as the standard for achieving interoperability in event logs and
event streams (1849-2016) [3]. Process-oriented data science has achieved im-
mense success in the recent years, especially in the field of process mining. It
has also led to the development of a rich set of tools and software, e.g., ProM
[8] in the research field. One key component of these tools is having the full sup-
port for importing and exporting event data in the XES format. This has been
achieved by OpenXES, the open source implementation of the XES standard in
the Java programming language. Moreover, knowing the level of support for the
F. Casati et al. (Eds.): Proceedings of the Dissertation Award and Demonstration, Industrial Track
at BPM 2018, CEUR-WS.org, 2018. Copyright © 2018 for this paper by its authors. Copying
permitted for private and academic purposes. This volume is published and copyrighted by its
editors.
2 H.Valdivieso, J.Lee, J.Munoz-Gama, and M.Sepúlveda
XES standard is such an important issue that one of the recent achievements of
the XES working group is the approval of the XES certification proposal. With
this, software can be certified for the level of support for the XES standard. At
the same time, the popularity of Python as a programming language for data
science has been rising in the recent years [6]. This has been due to the open
source nature of the community, the collection of packages oriented towards data
science, e.g., NumPy, SciPy, pandas, and matplotlib, and its high productivity
for prototyping and building small and reusable systems. In fact, the code im-
plementation of many recent research papers are in Python for precisely these
reason. However, despite the large collection of useful tools for data science in
the Python environment, this adoption trend is largely unseen in the field of
business process management. We believe one of the key reasons is the lack of
support in handling event data in the XES format. While there have been some
previous attempts to fill this gap, e.g., simple Python scripts that support parts
of the XES standard, or complex suites that are difficult to be used in stand-
alone prototyping, there has yet to be a full implementation of the standard. In
R, there are similar support for XES files with the BupaR packages [5]. However,
it does not support the full XES standard.
In this work we make a serious step towards that direction by presenting
OpyenXES, a complete open-source Python library for the XES standard. The
remainder of the paper is structured as follows: Section 2 presents the library
and the principles behind it. Section 3 illustrates the applicability of the library
with a set of different examples. Section 4 presents the links to access the library,
the source-code, and the documentation. Finally, Section 5 concludes the paper.
2 OpyenXES: The Library
OpyenXES is a Python library to support the creation, analysis, and storing
of event data satisfying the XES standard. OpyenXES was created with the
following principles in mind:
B Python: OpyenXES was implemented in Python for Python. It was designed
to be programmed as any Python script, using typical Python syntax, e.g.,
iterate the traces of a log using ‘for’, or add a new event using ‘append’.
Moreover, it is registered under the Python Package Index (PyPI) so that it
is pip installable.
B Complete: Unlike other Python scripts for XES, OpyenXES includes all the
elements defined in the XES standard [3], such as ‘classifiers’ and ‘exten-
sions’. Figure 1 shows the overview of the main components of the library.
Software using OpyenXES should have the same XES certification level as
current software using OpenXES.
B Open: OpyenXES was created following the Open Science Principle, like
other related tools such as OpenXES or ProM. Therefore, both the library
and the source code are publicly available (cf. Section 4). This opens the
door to the data science community to fork, correct, and improve the library
OpyenXES: A Python Library for XES 3
e.g., propose more efficient DB-based implementations for the storage and
processing of the XES logs [7].
B OpenXES Compatible: The state-of-the-art implementation for XES is the
OpenXES open-source Java library [1]. In order to ease the transition from
one library to another (and the other way around), OpyenXES has preserved
both the naming and the package structure of the more mature (cf. Figure
1). A researcher familiar with one library would be able to implement pro-
grams using the other library with little effort. Notice that, the name itself
(OpyenXES) is an homage paid to and its creators and maintainers for their
selfless work.
B Combinable: One of the main strength of Python is its data science and
machine learning libraries (NumPy, SciPy, pandas, matplotlib, jupyter, sci-
kit learn, . . . ). OpyenXES was designed to be combined with such libraries
in order to realize its potential as a data science tool.
Fig. 1. Overview of the main components of OpyenXES.
3 Maturity and Use Cases
The following set of examples illustrate the maturity, usefulness, and applica-
bility of OpyenXES for process-oriented data science and its combination with
other popular Python libraries. Due to space limitations, the source codes of all
the examples are available in https://github.com/opyenxes/OpyenXes/tree/
master/example.
B Log Anonymization: An event log is iterated through to remove any infor-
mation about the resources involved in the cases and the events.
B CSV to XES : Event data stored in a Comma Separated Value file (.csv) is
converted into a XES event log file, including the date transformation into
a XES compatible format.
4 H.Valdivieso, J.Lee, J.Munoz-Gama, and M.Sepúlveda
B Random Log: A log is created from scratch using random values.
B Filter Variants: A new event log is generated by only preserving one of the
traces from all the traces with the same sequences of activities, i.e., only the
different variants of the original event log.
B Alpha Algorithm: The classic process mining discovery algorithm Alpha Al-
gorithm [4] is easily implemented in Python using OpyenXES to generate
the footprints.
B Reporting Statistics: An event log is iterated through to obtain statistics
about the number of activities performed by each resource, and the results
are graphically plotted using matplotlib library.
B Trace Clustering: OpyenXES is combined with the popular machine learn-
ing Python library sci-kit learn to clustering the traces using the k-means
algorithm.
These examples only tease in an instructive way the full potential of Opy-
enXES and Python for process-oriented data science, opening the door to more
complex operations such as: k-anonymity, tool-specific format transformation,
Python specific libraries for CSV or JSON processing, event log simulation,
domain-specific filters, other process mining algorithms, or the combination with
well known data science python libraries.
Fig. 2. Documentation and source code of OpyenXES.
4 Download, Screencast, and Links
The library is available as a GitHub public repository under the organization
opyenxes (https://github.com/opyenxes) opening the door to be forked, ex-
OpyenXES: A Python Library for XES 5
tended, or improved by the community. The source code is also available in
the repository. The library is completely documented in http://opyenxes.
readthedocs.io/en/latest/?badge=latest (cf. Figure 2). A screencast illus-
trating the main features of OpyenXES is available in www.processmininguc.
com/tools. Finally, examples showcasing the use of the library are available in
https://github.com/opyenxes/OpyenXes/tree/2018-bpm-demo/example.
5 Conclusions
This paper presented OpyenXES, a Python library for the eXtensible Event
Stream (XES), a format used for the interchange of event log data between tools
and application domains. The library includes all the elements of the standard
(such as classifiers or extensions), it is open-source and open to the community,
and it respects the same naming and package architecture than the state-of-the-
art Java library, easing the transition from one library to the other. We believe
that OpyenXES could trigger the interest of a new set of data scientists for
research in BPM and process mining.
Acknowledgments. This work is partially supported by CONICYT-PCHA /
Doctorado Nacional / 2017-21170612, Vicerrectorı́a de Investigación de la Pon-
tificia Universidad Católica de Chile / Concurso Investigación Pregrado 2017,
and by the Departamento de Ciencias de la Computación UC / Fond-DCC-
2017-0001. The authors would like to thank the members of the IEEE Task
Force on Process Mining XES Working Group and the creators and mantainers
of OpenXES Java Library for their selfless efforts.
References
1. OpenXes, http://www.xes-standard.org/openxes/start
2. XES Web, http://www.xes-standard.org
3. IEEE Standard for eXtensible Event Stream (XES) for Achieving Interoperability
in Event Logs and Event Streams. IEEE Std 1849-2016 pp. 1–50 (Nov 2016)
4. van der Aalst, W.M.P.: Process Mining - Data Science in Action. Springer (2016)
5. Janssenswillen, G., Depaire, B.: bupar: Business process analysis in R. In: Proceed-
ings of the BPM Demo Track and BPM Dissertation Award co-located with (BPM
2017). (2017)
6. Puget, J.F.: What Language is Best for Machine Learning and Data
Science (2016), https://www.ibm.com/developerworks/community/blogs/jfp/
entry/What_Language_Is_Best_For_Machine_Learning_And_Data_Science
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63–77. CEUR-WS.org (2016)
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