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Event Log Extraction for the Purpose of Process Mining: A Systematic Literature
Review
Conference Paper · October 2019

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SEE PROFILEEvent Log Extraction for the Purpose of Process
Mining: A Systematic Literature Review
Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic
Nenad

Abstract Process mining bridges the gap between process model analysis and dataoriented analysis, such as data mining, by enabling automated discovery of processmodels, comparison of existing process models with an event log of the same process
and improvement of existing process models. Process mining prerequisite is an
information system that supports and controls real-life business processes andconsequently stores event data, such as messages, transactions, logs etc., as event logs
in some type of a database. Event data is then extracted, filtered and loaded into
process mining software, where certain type of process mining can be conducted.
Process-aware information systems (PAIS), which assume an explicit notion of a caseto correlate events of a process, provide such logs directly. However, many
information systems that support execution of business processes are not explicitly
process-aware and due to the variability of the event data sources, this phase of processmining is challenging and the most time-consuming. Consequently, various event log
extraction techniques, approaches and tools are being developed, both specific andgeneric. To make a contribution to the issue, this paper presents a systematic literature
review conducted with the aim to answer the questions about genericity of theapproaches, applicability by non-experts and developed feasible tools.

1 Introduction
Since the process-oriented view on organizations, some enterprise information
systems have adopted explicit process concepts and offer “generic modeling andenactment capabilities for structured business processes” [1]. These information
systems store data about process execution in some form. Process mining assumes that
it is possible to generate an event log from this data, such that “each event in the eventlog refers to an activity (i.e., a well-defined step in a process) and is related to aparticular case (i.e., a process instance)” [2]. However, not all PAIS record event datain such way. Beside this traditional Workflow Management Systems (WMS) andBusiness Process Management (BPM) systems, other information systems such asEnterprise Resource Planning (ERP) and Customer Relationship Management (CRM)
systems are rather data-centric and object-centric, meaning that “their processes
operate on multiple interrelated business objects, each having their own case identifier,their own behavior, and interaction with each other” [10].

Dakic Dusanka, Trg Dositeja Obradovica 6, dakic.dusanka@uns.ac.rs
Stefanovic Darko, Trg Dositeja Obradovica, darko.stefanovic@uns.ac.rs
Lolic Teodora, Trg Dositeja Obradovica 6, teodora.lolic@uns.ac.rs
Narandzic Dajana, Trg Dositeja Obradovica 6, narandzic@uns.ac.rs
Simeunovic Nenad, Trg Dositeja Obradovica 6, nsimeun@uns.ac.rs

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Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

Information about business objects (e.g. documents in ERP) are scattered throughvarious data tables [14, 19]. Therefore, extracting event logs from various data sources
is the most challenging phase of process mining and can present an obstacle, especiallyfor non-experts, i.e., business analysts that do not have programming knowledge. In
the last decade, approaches and corresponding tools were developed to support andautomate event log extraction. However, there is yet no generic approach that
researchers and practitioners agree on and each approach still has limitations. In
current literature, there is no consolidation of these approaches, their characteristics
and challenges, making it difficult for practitioners to conduct this initial phase ofprocess mining.
This paper presents the results of a systematic literature review on the event log
extraction approaches in the last ten years, conducted based on the guidelines for
performing systematic literature reviews in software engineering by Barbara
Kitchenham [2], with the focus on: Event log extraction approaches that non-experts
can apply; a possibility of a generic event log extraction approach, applicable toextracting data from every information system and feasible tools that support event
log extraction.
The remainder of the paper is organized as follows. Section 2 presents basic
concepts of process mining and event logs. Section 3 describes the systematic
literature review methodology. Section 4 presents conducted planning the review
phase of the systematic literature review. Section 5 presents conducting the review
phase of the systematic literature review, where primary studies were selected and thereview results presented. Section 6 discusses the systematic literature review resultsin detail and Section 7 concludes the paper and suggests future research.

2 Process Mining and Event Log Concepts
Process mining is a new research field, introduced and defined by the IEEE Task Forceon Process Mining in their Process Mining Manifesto [3]. They defined processmining as follows:“The idea of process mining is to discover, monitor and improve real processes(i.e., not assumed processes) by extracting knowledge from event logs readily
available in today’s (information) systems. Process mining includes (automated)
process discovery (i.e., extracting process models from an event log), conformancechecking (i.e., monitoring deviations by comparing model and log), social
network/organizational mining, automated construction of simulation models, model
extension, model repair, case prediction, and history-based recommendations.”
Fig. 1. presents prerequisites of process mining and a basic process mining procedure[3].
Fig. 1 Process mining procedure [3]Software systems support and control real-life business processes, machines,
components, organizations and people. Software systems are assumed to be
configured and specified by process models. Consequently, these software systems

Event Log Extraction for the Purpose of Process Mining: A Systematic Literature Review

record events, e.g., messages, transactions, etc. that are later extracted from various
data sources and constructed as event logs. Event logs are a starting point for
performing three basic types of process mining: Discovery, conformance andenhancement.
According to the IEEE Task Force on Process Mining, in a constructed event log,
it is assumed that [3]:“An event refers to a process activity or a task, which is a well-defined step inthe process and is related to a particular case, i.e. process instance;Events are ordered;

The case or process instance is a specific occurrence of a business process,while activity is an operation, part of a case, that is being executed;

Each case has a unique identifier;

An event log stores information about cases and activities, but also information
about resources (person or device used for execution of the activity), event
timestamps (moment when the event started and/or ended) or other data
elements recorded with the event.”

In 2010, the IEEE Task Force on Process Mining adopted XES (Extendable Event
Stream) as a standard for process mining event logs [3]. XES is an XML-based format
where every event log entry has an event type, a timestamp and other additional
attributes.
The Process Mining Manifesto [3] outlined the guiding principles and main challenges
of process mining. Guiding principles and challenges that relate to event logs and thescope of this literature review will be discussed. Guiding principles related to event
logs are:

GP1: Event Data Should Be Treated as First-Class Citizens;
GP2: Log Extraction Should Be Driven by Questions;
GP6: Process Mining Should Be a Continuous Process.

GP1 states that the quality of process mining is directly related to the quality of theevent log, i.e. the input for process mining. Therefore, the IEEE Task Force on Process
Mining suggests quality criteria that an event log should satisfy: “Events should betrustworthy, i.e., it should be safe to assume that the recorded events actually happened
and that the attributes of events are correct. Event logs should be complete, i.e., given
a particular scope, no events may be missing. Any recorded event should have welldefined semantics. Moreover, the event data should be safe in the sense that privacy
and security concerns are addressed when recording the events.”[1].
GP2 addresses the challenge of defining a scope of the process mining project, asdata sources from which event data will be extracted contain various information, it
is not trivial to decide what information to extract. Therefore, event log extraction
should be driven by questions.
GP6 suggest that both historical event data and current data can be used for processmining in order to generate “living process models”. Thus, an approach that enables
continuous event log extraction should also be a prerequisite.
The following challenges are related to event logs:
 C1: Finding, Merging, and Cleaning Event Data; C2: Dealing with Complex Event Logs Having Diverse Characteristics;
 C10: Improving Usability for Non-experts;
 C11: Improving Understandability for Non-experts.
The main C1 challenges regarding finding, merging and cleaning event data are:“Data may be distributed over a variety of sources; Event data are often “object
centric” rather than “process centric”; Event data may be incomplete; An event log

3Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

4

may contain outliers, i.e., exceptional behavior also referred to as noise; Logs may
contain events at different levels of granularity; Events occur in a particular context(weather, workload, day of the week, etc.)” [3].
Challenges C10 and C11 both accentuate that current process mining techniques may
be difficult to perform and understand for non-experts. Performance of process mining
techniques includes event log extraction phase, hence this challenge applies on theevent log extraction approaches as well.

3 Methodology
For the purpose of conducting this systematic literature review, a procedure forsystematic reviews developed by Barbara Kitchenham [2] was followed. According
to Kitchenham, a systematic literature review can be summarized into three main
phases: Planning the Review, Conducting the Review and Reporting the Review.

3.1 Planning the Review
Planning the Review phase has a goal to determine and elaborate the need for asystematic literature review and to develop a review protocol. The need for asystematic literature review can be established by reviewing existing literature
reviews in that particulair research area, following the guidelines proposed by
Kitchenham [2]. Once the need for a review is established, a review protocol can be
developed.
The review protocol shoud shortly present background and previously determinedrationale for the survey. The second component of the review protocol is to formulate
the research questions. A research question should, according to Kitchenham [2]: „bemeaningful and important to practitioners and researchers, lead either to changes incurrent software engineering practice or increase confidence in the value of the current
practice and identify discrepancies between commonly held beliefs and reality“.
Furthermore, question structure should take into consideration three different
viewpoints: „population, i.e. group of people affected by the review, interventions, i.e.
software technologies that adress specific issues and outcomes, i.e. factors ofimportance to practitioners“.
After the research questions are formulated, a search strategy for primary studies
based on initial scoping determines which search terms and databases will be used.
Study selection criteria needs to be established, in order to be used as inclusion andexclusion criteria for initial analysis of primary studies and consequently, excluding
studies from a systematic review.
The next step of the review protocol determines the quality assesment checklist thatwill be applied to the primary studies, included in the literature review. As
Kitchenham states, there is no unique definition of“quality”. However, other authors
such as the CRD Guidelines [4] and the Cochrane Reviewers’ Handbook [5] suggestthat „quality relates to the extent to which the study minimises bias and maximisesinternal and external validity“. By bias, a tendency to produce results that departsystematically from the ‘true’ results are considered. Internal validity is the extent towhich the design and conduct of the study are likely to prevent systematic error.
External validity, i.e. generalisability, applicability, is the extent to which the effects
observed in the study are applicable outside of the study. The last steps are definitionof strategy for the data extraction, synthesis of the extracted data and a project
timetable.

3.2 Conducting and Reporting the Review phaseEvent Log Extraction for the Purpose of Process Mining: A Systematic Literature Review

Once the protocol has been developed, the review can start. This involves thefollowing activities [2]:1. Identification of research;
2. Selection of studies;
3. Study quality assessment;
4. Data extraction and monitoring progress;
5. Data synthesis.
Reporting the Review is the final phase of a systematic literature review, where it
is important to communicate the results of a systematic review effectively. Usuallysystematic reviews are reported in at least two formats [2]:
 In a technical report or in a section of a PhD thesis;
 In a journal or conference paper.

4 Planning the Review
First activity of Planning the Review phase should be elaboration of the need for asystematic literature review, by reviewing existing literature review of the subject [2].
However, there are no explicit systematic literature reviews on the subject of event
log extraction, rather reviews of process mining field in general, its applicability, stateof the art and challenges [6, 7, 8, 9].
Other sources of information about event log extraction approaches could be therelated work chapters of studies that are pesenting a novelity on the subject. However,these stuides only mention previous event log extraction approaches relevant for their
particulair topic of interest. This systematic literature review presents summarized
information about current event log extraction approaches, their usability by nonexperts, genericity and developed tools, by answering the research questions.
Based on the guidelines from Kitchenham [2], following research questions areformulated:
Q1: Are there any event log extraction approaches that non-experts can apply?
Q2: Is there a generic event log extraction approach, applicable for extracting event
data from every type of information system?
Q3: Are there any feasible tools that support event log extraction?
For the purpose of this literature review, following databases where searched: SCOPUS,
 Web of Science and Google scholar.
Elsevier's Scopus is the largest abstract and citation database of peer-reviewed
literature. Web of Science provides access to reliable, integrated and multidisciplinary
research connected through linked content citation metrics from multiple sources
within a single interface.
Search term defined for search in these databases is presented below:“Process mining” AND “event log” AND extraction AND PUBYEAR > 2008
The inclusion criteria defined for this review are:
1. Paper has to present an approach for event log extraction for the purpose of processmining.

5

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Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

2. The approach has to include detailed information about developed procedure.
3. The study has to present a feasible approach, applicable in real-life scenarios.
Exclusion criteria defined for the review are:4. Duplicate papers found in different databases should be removed.
5. If one author had more than one paper regarding the same approach, only one paper
should be included in the review.
For the purpose of this literature review, the data extraction strategy was developed.
For each primary study, following features will be extracted, in order to answer
research questions:
1. Publication year and source type,2. Developed tool,
3. Feasibilty and applicabilty of the approach,
4. Genericity of the approach and information systems,
5. Applicability for non-experts.

5 Conducting the Review
5.1 Identification and selection of primary studies
First activity of conducting the review phase is identification of the research, i.e.
primary studies that will be included in the systematic literature review. Primarystudies are identified in accordance with inclusion and exclusion criteria, defined in
the review protocol. Fig. 2 summarizes quantitative evidence of the
inclusion/exclusion process in an appropriate flow diagram.
Fig. 2 Flow diagram of the exclusion and inclusion process

Initial search result → 220 primary studies → 38
duplicate papers excluded based on criteria 4 → 182
primary studies remaining

Review based on criteria 1 → 76 out of 182 primarystudies are included in the review

Review based on criteria 2 → 41 out of 76 primarystudies are included in the review

Review based on criteria 3 → 28 primary studies areincluded in the review

13 primary studies excluded based on criteria 5 → 15 outof 28 primary studies remaining for the review

Search through data sources with previously defined search term resulted in
identification of 220 primary studies. Based on exclusion criteria 4 (Duplicate papers
found in different databases should be removed) 38 duplicate papers where found andexcluded from further research. Review based on criteria 1 (Paper has to present anapproach for event log extraction for the purpose of process mining) was conductedon the title and abstract of the papers and resulted in inclusion of 76 primary studies.
Review based on criteria 2 (The paper has to include detailed information about the

Event Log Extraction for the Purpose of Process Mining: A Systematic Literature Review

7developed approach) included 41 primary studies in the literature review. Out of 41
primary studies, 28 satisfied the criteria 3 (The study has to present a feasible
approach, applicable in real-life scenarios). The last step of inclusion/exclusion
process excluded 13 papers based on criteria 5 (If one author had more than one paper
regarding the same approach, only one paper should be included in the review).
Finally, 15 primary studies are included in the literature review.

5.2 Data extraction and summarization
In this chapter, main characteristic of primary studies are summarized and presented,
following with the tables that present extracted data based on the data extraction
strategy.
Fig. 3 presents event log extraction approaches by years in which primary studies
that presented the approaches where published. The line diagram shows that two peaksin the number of published approaches occurred, one in 2012, two years after Process
Mining Manifesto [3] was published and one in 2017.
Fig. 3 Summary of event log extraction approaches by years

Event Log Extraction Approaches by Years
4,5
4
3,5
3
2,5
2
1,5
1
0,5
0
2009

2010

2011

2012

2013

2014

2015

2016

2017

2018

Table 1. presents source types of primary studies, showing that 40% of theapproaches were published as journal articles, following with 27% of conference
papers and master’s thesis and one published procedure for event log extraction. Threeout of four published master’s thesis were conducted at the Eindhoven University ofTechnology.
Table 1 Source types of primary studies
Source type
Journal Article
Conference paper
Master’s thesis
ProcedurePrimary studies
10, 15, 16, 17, 19, 20
12, 18, 22, 23
11, 13, 14, 24
21

%
40%
27%
27%
7%

In the review protocol three research questions where formulated. Table 2. presents
extracted data that answers the first question: Q1: Are there any event log extraction
approaches that non-experts can apply?

Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

8

Table 2 Primary studies and applicability of their approaches by non-experts
Applicable for nonexperts?
yesnoPrimary studies%

14, 16, 21
10, 11, 12, 13, 15,17, 18, 19, 20,
22, 23, 24

20%
80%

Table 2. shows that 20% of the reviewed approaches could be applied by a nonexpert, while 80% o the approaches require extensive programming knowledge.
Table 3. presents extracted data that answers the second question:
Q2: Is there a generic event log extraction approach, applicable for extracting datafrom every type of information system?
Table 3 Primary studies and information system types that their approaches deal with
Information system types

Primary studies%

ERP

10, 12, 13, 19, 21, 22, 23, 24

53%

Potentially generic14, 16, 17

20%

Cloud systems

11, 2013%

Non-process aware information systems

15

7%

Unstructured business processes

18

7%

During the data extraction phase, it was concluded that the reviewed event log
extraction approaches can be grouped by different information system types. ERP
systems, which are object and data centric systems, where used the most. Threeapproaches [14, 16, 17] showed potential to be generic, i.e. applicable for every data
source and information system type. Furthermore, two approaches dealth with theextraction of event logs from cloud systems [11, 20], one approach presented asolution for event log extraction from completely non-process aware information
systems [15] and lastly, [18] developed an approach for event log extraction frominformation systems that support unstructured business processes.
Table 4. presents information about tools and plug-ins that where developed inorder to automate the approaches of event log extraction, sorted ascending by the year
in which they were developed and with additional information about the event log
format that they geneate as a result of event log extraction process. The third question(Q3: Are there any feasible tools that support event log extraction?) adresses the issueof the feasability of these tools. For each tool or plug-in developed, in corresponding
primary study at least one case study was presented in order to demonstrate theapplicability of the approach. Therefore, altough all tools and approaches have certain
limitations, they are considered to be feasible.
Table 4 Developed tools and plug-ins with event log format
Primary
studiesYear

Event log format

XES Mapper
Prototype for event log
extraction from SAP ECC
6.0
Xtract14

2010

XES and MXML

13

2011

CSV

12

2012

XES

Event-traces Injector (ETI)Eventifier
MANA
Xtract V2

15
17
18
10

2012
2012
2013
2015

MXML
XES
XES
XES

Tools/plug-ins

Event Log Extraction for the Purpose of Process Mining: A Systematic Literature Review

9

Xtract V3

11

2017

XES

Ontology-driven extraction
plug-in for ProM

23

2017

XES

Process mining tool and ERP
connector

24

2017

XES

OpenSlex and PADAS

16

2018

XOC Log Generator Plugin
in ProM 6

22

2018

new format, convertible to
XES
eXtensible Object-Centric
event logs (XOC)

6 Discussion
This section discusses the results of the conducted systematic literature review,presented in section 5.
The highest number of the reviewed approaches where published in the years 2012
and 2017. The previously mentioned Process Mining Manifesto was published in2012. by the IEEE Task Force on Process Mining, hence the high number ofapproaches published that year. By the year 2017 these first event log extraction
approaches where utilizied in the inudustry and showed limitations and challenges, aswell as reasons for improvement. The approaches developed in 2017 and after, tend
to solve this challenges. Most of the approaches were published in journal articles.
However, a significant number of approaches were developed through master’s thesisat the Eindhoven University of Technology, from where the idea of process mining
originated.
For the purpose of answering the research question Q1, approaches that tend to
improve usabilty of event log extraction by non-experts were identified. J.C.A.M.
Buijs states in his master’s thesis [14] that there is no tool that guides a businessanalyst towards converting data from a data source to an event log format suitable forprocess mining without the need to program and therefore creates a tool XES Mapper(later called XESame1). In [16] the authors recognized the need for a meta model thatwill integrate process and data perspectives and enable multi-perspective event-log
building and analysis without the knowledge of SQL. In [21] the authors developed aprocedure that leads a process analysts with limited knowledge of process mining
though event log extraction process, increases the analyst’s understanding of thedecisions and their consequences, related to the choice of process instance, activities
and attributes and provides the process analyst with a practical example or sufficient
background information in order to conduct the event log extraction approach.
The question Q2 is answered positively, as there are three approaches [14, 16, 17]that can potentially be applied for every type of information system. The primarystudies that reported the approaches presented case studies in which event log
extraction is conducted on data sources of different information systems or at least
provided a guideline for a generic application of their event log extraction approaches
and tools. Two of these stuides [14, 16] where also found to be applicable for nonexperts.
On the other hand, most approaches were developed for event log extraction from
ERP systems [10, 12, 13, 19, 21, 22, 23, 24]. ERP system are object-oriented, i.e.
along business documents, and not along business processes. To find the process data
behind the static data, the business documents and related activities need to bematched into cases, i.e. the same execution of a process. Therefore, a suitable way ofcase identification has to be found which is also able to handle complex cases. Twomain problems were identified while performing event log extraction from ERP
systems: convergence and divergence [10, 11, 12, 13, 14, 22, 24]. In [14] the author
defines them as follows: Convergence occurrs when the same activity is executed on
1

http://www.processmining.org/xesame/startDakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

10

multiple process instances at onc; Divergence occurrs when for one process instancethe same activity is performed multiple times. Divergence and covergence remain
unsolved problems, altough several primary studies that developed artifact-centric
approaches suggested how to avoid these problems in particulair situations [10, 11,
14, 24].
As question Q3 is considered, various tools and plug-ins where developed toautomate the process of event log extraction, as presented in table 4.

7 Conclusion
With todays highly automated business environment, amount of recorded data is
rapidly growing. Correspondingly, new research disciplines that exploit this data, suchas process mining, are emerging. Being a relatively new research discipline, processmining encounters challenges. This paper presented a systematic literature review ofthe most challenging phase of process mining – event log extraction. Existing
approaches are identified and the following research questions are answered: Q1: Are
there any event log extraction approaches that non-experts can apply? Q2: Is there ageneric event log extraction approach, applicable for extracting event data from every
type of information system? Q3: Are there any feasible tools that support event log
extraction?
It is concluded that several approaches do provide higher understandability andusability by non-experts, however this is still a small number of approaches.
Furthermore, some approaches do have a potential to be generic, i.e. applicable forevery information system type. As feasible tools are considered, there are many
developed protoypes and plug-ins that researchers can use to automate the event log
extraction phase of process mining.
Future work should focus on detected challenges, such as convergence anddivergence, continous event log extraction, improving usability for non-experts andpresent possible soutions.
ACKNOWLEDGMENT
This article has been produced as part of a research project: no. 47028 “Advancing
Serbia's competitiveness in the EU accession process” supported by the Ministry of
Education, Science and Technological Development of the Republic of Serbia for theperiod 2011th-2019th year.

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Degree project in information and communication technology, KTH Royal

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Dakic Dusanka, Stefanovic Darko, Lolic Teodora, Narandzic Dajana, Simeunovic Nenad

Institute of Technology, School of Information and Communication
Technology, Stockholm, Sweden

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