=Paper=
{{Paper
|id=Vol-2839/paper4
|storemode=property
|title=Towards a Framework for Data Enhanced Process Models in Process Mining
|pdfUrl=https://ceur-ws.org/Vol-2839/paper4.pdf
|volume=Vol-2839
|authors=Jonas Cremerius
|dblpUrl=https://dblp.org/rec/conf/zeus/Cremerius21
}}
==Towards a Framework for Data Enhanced Process Models in Process Mining==
https://ceur-ws.org/Vol-2839/paper4.pdf
Towards a Framework for Data Enhanced
Process Models in Process Mining
Jonas Cremerius
Hasso Plattner Institute, University of Potsdam, Potsdam, Germany
jonas.cremerius@hpi.de
Abstract. Understanding and improving business processes have become
important steps towards success for organizations. Getting insights about
a process is not only based on the control flow, but also on the data
generated within the process. Today, almost every process step generates
data, especially in the health care domain. So far, most analyzes inside a
process model are limited to time analyzes and identification of decision
logic. However, various attributes can be linked to events, such as the
number of abnormal lab values derived from a lab test, which could be
displayed in the process model. This can help to explore the process with
domain experts, where they can choose the attributes of interest for each
event and observe their influence on the process, i.e. the influence of
abnormal lab values on the treatment process. Therefore, the interplay
of event attributes and the control flow can be observed directly in the
process model.
Keywords: Process Mining · Process Enhancement · Process Model
Extension
1 Introduction
Business process models play a central role in exploring and analyzing the
organization’s business processes. With the help of process mining, process models
can be derived from real-world process execution data [3]. Process mining is often
conducted in the healthcare sector, as hospitals are becoming increasingly aware
of the need to improve their processes [8]. Despite the increasing availability
of data, adequate support for displaying or analyzing event attributes in a
discovered process model is still lacking. So far, process enhancement inside the
discovered process model is limited to time analyzes and decision logic, whereas
organizational aspects are separately analyzed [15]. This is also represented
in today’s process mining tools. Fluxicon Disco1 provides time analyzes only,
whereas Lana Labs2 , Celonis3 , and ProM4 do not provide attribute analyzes
1
https://www.fluxicon.com/disco/
2
https://www.lanalabs.com/
3
https://www.celonis.com/
4
https://www.promtools.org/
J. Manner, S. Haarmann, S. Kolb, N. Herzberg, O. Kopp (Eds.): 13th ZEUS Workshop,
ZEUS 2021, Bamberg, held virtually due to Covid-19 pandemic, Germany, 25-26 February 2021,
published at http://ceur-ws.org
Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
20 Jonas Cremerius
inside the process model. Only Process Diamond5 allows displaying one attribute,
which must be the same for all events. In several treatment processes, such as the
diagnosis and treatment of heart failure, a large amount of data is generated [1].
Example events include “Analyze Lab Values” and “Treat Patient in Intensive
Care Unit (ICU)”. For both events, a different set of attributes is produced,
such as the number of abnormal lab values and the Glasgow Coma Scale (GCS).
Considering the effect of these attributes on a process may provide insights
about a certain diagnostic or treatment. As current process mining tools are
not capable of exploring a process in respect to attribute values, we propose
a respective functionality that enables business analysts and domain experts
to identify conspicuous behavior not only in the control flow, but also in the
attribute values.
2 Related Work
Process Enhancement is the extension or improvement of an existing process
model using information about the actual process recorded in some event log [3].
There exist several approaches which analyze the activity duration and waiting
time in a process model to identify, for example, bottlenecks [10, 14]. Additional
analyzes inside the process model include identification of constraints at decision
points [7, 11]. Further, Process Enhancement involves the organizational analyzes
by looking at the assignment of human resources to process activities [12].
Enhancement is also conducted offside the process model where, for instance,
different process characteristics are correlated with each other [5, 13].
Case and event attributes have been used in filtering process variants and
clustering of traces. For instance, [4] clusters traces to discover process variants
by different case and event attributes, such as age and the body part of an image
analyzes.
3 Research Objective
None of the approaches mentioned above looked into displaying event attributes
directly in the process model. So far, they are limited to time analyzes and
identification of decision points. Our research aims to fill this gap and provides a
framework to link event attributes with their respective event in the process model.
Today’s data sets, such as MIMIC-IV, include various event attributes, such as
the number of abnormal lab values [2]. As processes can get complex, different
attributes might be interesting for the events. An example process is displayed
in Fig. 1, which illustrates a simple treatment process. The events have their
attributes attached in the process model. The first two events ”Perform X-Ray”
and ”Perform CT” show the frequency of findings in specific body regions (heart
and lung). After that, the lab values are analyzed, which show the frequency of
abnormal lab values observed for each lab value. Then, the treatment is conducted,
5
https://www.processdiamond.com/
Towards a Framework for Data Enhanced Process Models 21
which can happen in the Intensive Care Unit (ICU) or Cardiology, where the
mean Glasgow Coma Score (GCS) is shown. This process can be similar for
several diseases on this abstraction level. Therefore, the insights regarding the
control flow might be limited. However, the event attributes can be different, as
the glucose level is more interesting for type 2 diabetes and the creatinine level
for kidney disease [6, 9]. This could help to assess how meaningful analyzing a
specific lab value for several diseases is. Furthermore, the effect of an attribute
on the process can be explored with the help of a process model. For example, if
the process of patients with an abnormal glucose level is of interest, one could
just click on the attribute, triggering filtering according to the lab value. Then,
not only the change in the process flow, but also in the event attributes can be
seen, such as the mean GCS or the frequency of findings in the heart region of an
image analyzes, which might be different for patients with an abnormal lab value.
This could reveal novel insights, as one might not have thought that patients
with an abnormal lab value also have a high prevalence of findings in the heart
region of an X-ray.
We want to enable this kind of analyzes in the process model, which could
lead to a more comprehensive exploration of processes together with a domain
expert. The framework defines how different types of attributes are displayed in
the process model and which computations can be performed on them, such as
minimum, maximum, or mean. As process attributes can be different depending
on the application context, we want to enable the domain experts to choose the
attributes of interest. Nevertheless, an attribute recommendation system could
be implemented, which helps to choose the appropriate attributes for an activity
based on machine learning or descriptive statistics. Additionally, the framework
could help to highlight events sharing the same attributes, such as different lab
values or medical imaging techniques. Therefore, the following research questions
need to be answered:
– How can event attributes be displayed in the process model (categorical vs.
continuous variables)?
– How can the framework help to gain new insights about the process (detection
of process variants, dependencies between attributes, etc.)?
22 Jonas Cremerius
Fig. 1. Process model with data attributes displayed for each event. CT, Computed
Tomography; ICU, Intensive Care Unit; GCS, Glasgow Coma Score
4 Conclusion
This position paper discusses the need for looking at the inclusion of event
attributes directly in the process model. With the increasing data availability, a
more comprehensive view of the process is possible and different process variants
can be explored. As process models can be used as a means of communication
between process analysts and domain experts, incorporating event data in the
model has the potential to improve that communication by illustrating the control
flow and event attributes in one place.
Towards a Framework for Data Enhanced Process Models 23
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