Event Log Knowledge as a Complementary Simulation Model
Construction Input
Niels Martin
1
, Benoît Depaire
1,2
and An Caris
1,2
1
Hasselt University, Agoralaan – Building D, 3590 Diepenbeek, Belgium
2
Research Foundation Flanders (FWO), Egmontstraat 5, 1000 Brussels, Belgium
Keywords: Business Process Simulation, Event Log Knowledge, Process Mining, Simulation Model Construction
Inputs, Conceptual Framework.
Abstract: Business process simulation models are typically built using model construction inputs such as
documentation, interviews and observations. Due to issues with these information sources, efforts to further
improve the realism of simulation models are valuable. Within this context, the present paper focuses on the
use of process execution data in simulation model construction. More specifically, the behaviour of
contemporary business processes is increasingly registered in event logs by process-aware information
systems. Knowledge can be extracted from these log files using process mining techniques. This paper
advocates the addition of event log knowledge as a model construction input, complementary to traditional
information sources. A conceptual framework for simulation model construction is presented and the
integration of event log knowledge during the modeling of particular simulation model building blocks is
outlined. The use of event log knowledge is demonstrated in a simulation of the operations of a roadside
assistance company.
1 INTRODUCTION
Business process simulation (BPS) refers to the
imitation of business process behaviour through the
use of a simulation model (Melão and Pidd, 2003).
By mimicking the real system, simulation can
contribute to the analysis and potentially the
improvement of business processes (Rozinat et al.,
2008).
Simulation models are typically created by
simulation experts based on insights from traditional
information sources such as process documentation,
interviews and observations. Issues with these
information sources, as will be outlined in section
2.1, may contribute to the discrepancy between the
constructed simulation model and reality (Rozinat et
al., 2009). Consequently, efforts to further improve
the realism of simulation models are valuable as
they will enhance the representativeness of analysis
results and hence its relevance for management
support. The present paper focuses on
complementing traditional information sources with
insights from data depicting actual process
behaviour in an effort to construct more realistic
simulation models.
With regards to process behaviour data, note that
contemporary business processes are increasingly
supported by process-aware information systems
(PAIS) such as customer relationship management
systems and enterprise resource planning systems.
This type of systems register highly relevant
information on the actual behaviour of the process
under consideration in files called event logs. These
files can be analyzed through the use of process
mining techniques (van der Aalst, 2011). To date,
this source of information is not used altogether or
tends to be underexploited in BPS model
construction.
This paper presents a conceptual framework for
simulation model construction taking both
traditional information sources and event log
knowledge into account as complementary inputs.
Moreover, some modeling aspects are detailed
where event log analysis can deliver a contribution.
The remainder of this paper is structured as
follows. In the second section, the aforementioned
conceptual framework is justified and presented. The
third section highlights some simulation model
building blocks where event logs can provide
valuable insights during the modeling process. An
456
Martin N., Depaire B. and Caris A..
Event Log Knowledge as a Complementary Simulation Model Construction Input.
DOI: 10.5220/0005100404560462
In Proceedings of the 4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH-2014),
pages 456-462
ISBN: 978-989-758-038-3
Copyright
c
 2014 SCITEPRESS (Science and Technology Publications, Lda.)

illustrative simulation study is outlined in the fourth
section. The paper ends with a discussion and
conclusions.
2 CONCEPTUAL FRAMEWORK
FOR SIMULATION MODEL
CONSTRUCTION
This section introduces a conceptual framework
regarding BPS model construction. The model is
justified in section 2.1 to support its relevance.
Section 2.2 presents the actual framework.
2.1 Framework Justification
Various information sources are used to gain
insights in a business process when constructing a
simulation model. These typically include process
documentation, interviews with business experts and
the observation of real-life processes (Rozinat et al.,
2009).
However, the obtained information from these
sources might be biased. According to Măruşter and
van Beest (2009), process documentation might
deviate from real-life process behaviour. Interviews
with business experts can result in contradictory
information (Vincent, 1998) and their perception, as
human perception in general, tends to be biased to a
certain extent (Pronin, 2007). Observational data, in
their turn, could suffer from the Hawthorne effect,
which refers to the performance increase of staff
members due to the attention they receive as their
actions are observed (Brysbaert, 2006).
These issues contribute to a discrepancy between
modelled and real process behaviour (Rozinat et al.,
2009). Consequently, further efforts to improve the
realism of simulation models are required. One
approach could be the use of process execution data
embedded in event logs. These data reflect the real-
life behaviour of the process and can provide
valuable insights for model construction purposes.
To date, the use of process execution data in BPS
models is often limited to model parameterization
(Law, 2007), e.g. the estimation of entity arrival
rates and activity service times. The present paper
advocates a broader and more systematic use of
process execution data as a complementary input in
simulation model construction. Besides distribution
estimation, event data can also provide information
about the order of activities, the model’s decision
logic, etc.
2.2 Conceptual Framework
This section outlines a conceptual framework
towards simulation model construction which
introduces event log knowledge as an additional
input. A visual representation of the framework is
presented in figure 1.
When disregarding event log knowledge, a
simulation model is constructed based on
information sources such as business documents,
interviews with business experts and observations.
These model construction inputs are aligned and
analyzed to come up with a partial simulation model,
i.e. a simulation model ‘under construction’.
When knowledge hiatuses are identified during
the construction, the modeler can return to the inputs
and e.g. conduct additional expert interviews. This is
visualized by connecting the partial simulation
model to the model construction inputs in figure 1.
Another issue that might lead to the use of this
connection is the presence of non-tolerable
differences between process performance metrics in
the partial simulation model on the one hand and
reality on the other hand. Deviating values of e.g.
average throughput time will necessitate the addition
or adjustment of particular simulation model
parameters or other model components.
Consequently, a return to the model construction
inputs will be needed. The two outlined issues show
that it might require several iterations before the
simulation model is completed and the modeler can
pursue towards running the final model and
interpreting the results.
The conceptual framework introduces an
additional model construction input: event log
knowledge. To understand the origins of this extra
input, note that contemporary business processes are
increasingly supported by systems such as customer
relationship management systems and enterprise
resource planning systems. This type of systems are
called process-aware information systems (PAIS)
because the process notion is embedded in them. In
contrast, a standard e-mail system can support a
business process, but is not aware of the process it
backs. Consequently, it is not a PAIS (van de Aalst,
2011). To be able to support a business process, the
structure of the PAIS has to mimic the real-life
process structure to a large extent.
From PAIS, event logs can be retrieved which
capture process execution data. An illustrative
example of an event log of a roadside assistance
company is given in table 1. Each line in the log file
corresponds to a single event, e.g. the registration of
a customer request. Additional information on the
event can be recorded such as a timestamp and the
EventLogKnowledgeasaComplementarySimulationModelConstructionInput
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resource which is associated with the event. Events
should be related to a particular case, e.g. a customer
request. In simulation terms, a case corresponds to
an entity flowing through the process.
Table 1: Illustration of an event log.
Case Event Timestamp Activity Resource …
1 72 11/05/2014
12:03
Request
receipt
John …
73 11/05/2014
12:05
Request
transmission
John …
78 11/05/2014
12:28
Request
acceptance
Frank …
… … … … …
2 135 11/05/2014
14:12
Request
receipt
Alice …
138 11/05/2014
14:17
Request
transmission
Patrick …
164 11/05/2014
14:41
Patrolman
departure
Peter …
… … … … …
As the event log reflects the actual behaviour of
the real-life process, it contains valuable intelligence
for the construction of simulation models. In order to
extract usable knowledge from an event log, process
mining can be applied, which refers to the extraction
of process knowledge from event logs.
This might relate to the order of activities in the
process, the identification of resource roles, etc. (van
de Aalst, 2011). Some applications of process
mining techniques in a simulation context are
outlined in section 3. For an elaborate overview of
process mining, the reader is referred to van der
Aalst (2011).
The extracted event log knowledge serves as an
additional input for simulation model construction.
As mentioned in figure 1, event log knowledge has
the ability of complementing and cross-checking
traditional information sources. The analysis of
event logs might allow the discovery of an
alternative activity order, besides the common
activity flow derived from traditional information
sources. In this way, event log knowledge
complements expert interviews, etc. With regards to
cross-checking, event data analysis enables the
verification of e.g. uncertain statements of business
experts concerning particular model elements. Note
that a complementing and cross-checking
relationship should also exist among the traditional
information sources themselves. These relationships
are not visualized in figure 1 to maintain its clarity.
An event log can also serve as an initial
simulation model construction input. In this respect,
the analysis of the event log and initial modeling
efforts might e.g. lead to the identification of
specific questions that should be directed to a
Figure 1: Conceptual framework.
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business expert. Consequently, a reiteration occurs
from the partial simulation model to the model
construction inputs, in this case expert interviews.
Besides the identification of appropriate questions,
gaining initial process insights using event log
analysis can improve the quality of the expert
interview’s output. This statement is in accordance
with Pfadenhauer (2009) as it is deemed important
that the interviewer is knowledgeable about the
matter of discussion in order to retrieve valuable
information of an expert interviewee.
A final remark regarding the conceptual
framework relates to the fact that the framework
does not advocate for the construction of a
simulation model solely based on process execution
data due to technical and behavioural issues. On a
technical level, process mining outputs might render
rather complex results when applied to a real-life
process. Consider the use of process mining
techniques to discover the activities in a process and
their relationships, as will be detailed in section 3.2.
When these methods are applied to rather
unstructured processes, this might result in a process
model with a so called spaghetti structure. The latter
is characterized by activities with a large number of
mutual interconnections, rendering the process
model difficult to understand (van der Aalst, 2011).
Besides output complexity, a modeler should also be
aware of obstacles such as registration errors or
inaccuracies in the log files.
With regards to behavioural issues, building a
simulation model solely from an event log might
become a black box for business experts and
decision makers as they may not be familiar with the
used process mining techniques. Moreover, a
simulation model can be provided which is out of
scope with the personal process insights of the
business experts. When this is the case, the
acceptance and hence the use of the simulation
model might be hindered. The latter is, amongst
others, supported by the confirmation bias concept,
which refers to the human tendency to ignore or
criticize information that contradicts their proper
beliefs (Zimbardo et al., 2005).
3 EVENT LOG KNOWLEDGE IN
SIMULATION MODEL
CONSTRUCTION
The previous section presented a conceptual
framework advocating the integration of event log
knowledge as an additional and complementary
input for simulation model construction. The
inclusion of event log knowledge was mentioned in
rather generic terms. This section provides
additional insights in the use of event log knowledge
in a simulation model construction setting.
Furthermore, preliminary research efforts within this
domain are outlined.
A simulation model is composed of several
building blocks. Based on a literature review, several
generic simulation model components are identified.
The discussion in this section is organized using
some key simulation model building blocks. Due to
space limitations, the present paper will focus on
entities and interrelated activities. Nevertheless,
event log knowledge can also have a valuable
contribution when modeling attribute values,
resources and the simulation model’s decision logic.
The latter two are key points on our research agenda.
3.1 Entities
An entity is a dynamic object that is created within
the simulation model, moves throughout the
organizational system and afterwards typically
leaves the system. Entities cannot be considered as a
homogeneous set of objects: various entity types can
coexist within the model (Law, 2007).
The definition of entity types can be useful as the
followed order of activities, activity duration
distributions, etc. might differ for each entity type.
Moreover, distinguishing entity types allows for the
retrieval of decomposed performance statistics as the
introduced distinction is typically maintained when
performance metrics are generated.
Business experts might find it difficult to define
the appropriate number of relevant entity types with
respect to the objective of the simulation study.
Within this context, event logs can provide useful
insights as they contain information on the activity
order of a particular entity and possibly the activity
duration for this entity. Moreover, entity attributes
might be recorded as case attributes. Entities
following similar routes in the process or having
converging attribute values can be grouped. These
clusters can be perceived as possible entity types for
the process under consideration and could form a
starting point for a discussion with business experts.
Despite the potential benefits of process mining in
entity type discovery, no research efforts are
identified on this matter. Consequently, the
identification of entity types is one of the focal
points on our research agenda.
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3.2 Interrelated Activities
Once entities have entered the organizational
system, they flow through a series of interrelated
activities which provide a particular service to them
(Tumay, 1996).
To model process activities and their
relationships, the modeler might resort to a
traditional information source such as expert
interviews. Even though discussions with business
experts will provide valuable knowledge regarding
interrelated activities modeling, some aspects such
as deviating activity orders might not become
apparent. This type of process behaviour might
however be relevant to include in the simulation
model if it has a non-negligible impact on the
operation of the process. As event logs contain
information on activity orders, log analysis can
provide valuable insights in this respect.
Several algorithms have been developed to
discover process models from event logs. Some of
these techniques have been applied to determine the
order of activities in a simulation model; e.g. the
alpha-algorithm (Rozinat et al., 2009), heuristic
mining (Măruşter and van Beest, 2009) and fuzzy
mining (van Beest and Măruşter, 2007). For more
technical details on these algorithms, the reader is
referred to van der Aalst (2011).
Publications on this matter tend to consider fairly
simple processes. This observation can possibly be
attributed to the rather conceptual nature of the
literature in this domain, where activity order
discovery serves as a proof-of-concept for the
proposed methodology. As indicated in the final
paragraph of section 2.2, process mining algorithms
might generate results with a high degree of
complexity. The modeler can however also use
process discovery techniques to prepare expert
interviews or improve the activity order determined
through the use of traditional information sources.
4 ILLUSTRATIVE SIMULATION
STUDY
To demonstrate that the combination of traditional
information sources and event log knowledge can be
valuable during simulation model construction, this
section outlines an illustration based on real-life
data. For reasons of confidentiality, the illustration
has been anonymized.
The company under consideration provides
roadside assistance services, i.e. the company offers
its members assistance when confronted with a car
or motorcycle breakdown. If the latter occurs, a
member can contact the central dispatching center.
Consequently, the assistance request is transmitted
to a patrolman who is responsible for the repair. To
limit the inconvenience for the motorists, the
company guarantees its members that for 90% of the
requests, a patrolman will be present to help them
within 45 minutes.
The roadside assistance company was, amongst
others, interested in the effects of a changing
workforce size on the operational performance. For
this purpose, a simulation model needed to be
constructed. During the development of the
simulation model, both event log knowledge and
expert knowledge were used. The provided event log
contained information on the activities of the central
dispatching center on the one hand, e.g. the receipt
of an assistance request, and the patrolmen on the
other hand, e.g. the arrival at the requested location.
Expert knowledge was obtained during a meeting
with a staff member of the roadside assistance
company.
Simulation model construction efforts have
shown that event log insights and expert knowledge
are complementary. Consider e.g. the determination
of input parameters such as service time
distributions. The use of event logs allowed for a
more accurate parameter estimation than the rather
rough approximations a business expert would have
been able to provide. Expert input can however be
valuable to avoid the use of distorted service time
distributions due to the inclusion of extreme outliers
caused by registration errors.
Another example showing the complementarity
among event log and expert knowledge relates to the
activities comprising the process and their
relationships. The analysis of the event log
highlighted a dominant path that about two thirds of
the assistance requests follow: request receipt –
request transmission to patrolman – request
acceptance by patrolman – patrolman departure –
patrolman arrival – assistance termination – return of
patrolman. This might also be the business experts’
response when requested to describe the process.
However, other activity orders with a less
straightforward interpretation could be identified
from the event log. Some of these activity patterns
might reflect relevant process behaviour which
should be included in the simulation model, while
other activity orders are a consequence of
registration errors or inaccuracies. By engaging in a
dialogue with the business expert, the modeler could
determine which patterns had to be included in the
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simulation model. Adding additional relevant
patterns will enhance the realism of the simulation
model and consequently the usefulness of the
analysis results.
The examples in the previous paragraphs show
that event log knowledge can provide valuable
additional insights that can be integrated in the
simulation model. Moreover, event log analysis can
allow the modeler to identify relevant questions and
discussion topics which should be directed to the
business expert. In this sense, traditional information
sources and event log knowledge should not be seen
as adversaries but as complementary model
construction inputs.
5 DISCUSSION AND
CONCLUSIONS
Business process simulation models are typically
created based on insights from traditional
information sources such as process documentation,
interviews and observations. Issues with these model
construction inputs contribute to the discrepancy
between the constructed simulation model and
reality (Rozinat et al., 2009). The use of process
execution data, representing real-life process
behaviour, can be a valuable instrument to bridge the
gap between model and reality to a certain extent.
This paper advocates the use of event log
knowledge as an additional and complementary
simulation model construction input. Event log
knowledge is obtained by applying process mining
techniques to event logs, i.e. files capturing process
execution data which are retrieved from a PAIS. A
conceptual simulation model construction
framework is presented which includes this
additional input. Moreover, the discussion of the
framework and the illustration in section 4 highlight
the complementarity of event log knowledge and
traditional information sources.
The integration of event log knowledge in
simulation model construction is mentioned in rather
generic terms in the conceptual framework. Some of
the scarce publications regarding the use of process
mining in a simulation context have been outlined.
Our research agenda includes further efforts to
benefit from event log knowledge with regards to
the modeling of entity types, decision logic and
resources. Specific domains in which new
techniques are planned to be developed or existing
research will be leveraged includes entity type
discovery, the determination of priority rules in
queues and the modeling of human resources
behaviour.
It should be noted that current publications on
the use of process mining in a simulation context
tend to limit themselves to state how process mining
can be useful when constructing a simulation model.
However, they typically fail to recognize that event
log analysis results can be overly complex or contain
inconsistencies due to registration errors.
Consequently, the obtained event log knowledge
needs to be cross-checked with business experts.
This paper explicitly states that event log
knowledge is complementary to traditional
information sources and that both can strengthen
each other. In this way, a more realistic, but
manageable, simulation model can be created
compared to a situation in which traditional
information sources or event log knowledge are used
in isolation. An improvement of the simulation
model realism will lead to more representative
analysis results and hence more adequate decision
support.
REFERENCES
Brysbaert, M. (2006). Psychologie. Gent: Academia Press.
Law, A.M. (2007). Simulation modeling & analysis. New
York: McGraw-Hill.
Măruşter, L. and van Beest, N.R.T.P. (2009). Redesigning
business processes: a methodology based on
simulation and process mining techniques. Knowledge
and Information Systems, 21(3), 267-297.
Melão, N. and Pidd, M. (2003). Use of business process
simulation: a survey of practitioners. Journal of the
Operational Research Society, 54(1), 2-10.
Pfadenhauer, M. (2009). At eye level: the expert interview
- a talk between expert and quasi-expert. In Bogner,
A., Beate, L., Menz, W., (Eds.). Interviewing experts.
New York : Palgrave Macmillan, pp. 81-97.
Pronin, E. (2007). Perception and misperception of bias in
human judgment. Trends in Cognitive Sciences, 11(1),
37-43.
Rozinat, A., Mans, R. S., Song, M. and van der Aalst,
W.M.P. (2009). Discovering simulation models.
Information Systems, 34(3), 305–327.
Rozinat, A., Wynn, M.T., van der Aalst, W.M.P., ter
Hofstede, A. and Fidge, C.J. (2008). Workflow
simulation for operational decision support using
design, historic and state information. Lecture Notes in
Computer Science, 5240, 196-211.
Tumay, K. (1996). Business process simulation.
Proceedings of the 1996 Winter Simulation
Conference, Coronado, USA, December 1996, 55-60.
Van Beest, N.R.T.P. and Măruşter, L. (2007). A process
mining approach to redesign business processes – a
case study in gas industry. Ninth International
EventLogKnowledgeasaComplementarySimulationModelConstructionInput
461

Symposium on Symbolic and Numeric Algorithms for
Scientific Computing, 541-548.
Van der Aalst, W.M.P. (2011). Process mining: discovery,
conformance and enhancement of business processes.
Heidelberg: Springer-Verlag.
Vincent, S. (1998). Input data analysis. In Banks, J. (Ed.),
Handbook of simulation: principles, methodology,
advances, applications and practice. Hoboken: John
Wiley & Sons, pp. 3-30.
Zimbardo, P.G., Weber, A.L., and Johnson, R.L. (2005).
Psychologie: een inleiding (S. Birnie, Trans.).
Amsterdam: Pearson Education. (Original work
published 2003).
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