=Paper=
{{Paper
|id=Vol-3299/Paper02
|storemode=property
|title=Multi-Perspective Analysis of Process Dynamics (Extended Abstract)
|pdfUrl=https://ceur-ws.org/Vol-3299/Paper02.pdf
|volume=Vol-3299
|authors=Alexander Kraus
|dblpUrl=https://dblp.org/rec/conf/icpm/Kraus22
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==Multi-Perspective Analysis of Process Dynamics (Extended Abstract)==
https://ceur-ws.org/Vol-3299/Paper02.pdf
Multi-Perspective Analysis of Process Dynamics
(Extended Abstract)
Alexander Kraus
Data and Web Science Group, University of Mannheim, Schloss, 68161 Mannheim, Germany
Abstract
Many traditional process mining tasks, like process discovery and conformance checking, consider
processes as static, unchanging systems over time. However, in reality business processes are subject
to frequent changes due to the dynamic environment in which organizations operate. For many process
mining problems, it is important to account for these changes by analyzing business processes as
dynamic systems. In this PhD thesis, we address process mining challenges in which multi-perspective
analysis of process dynamics provides valuable insights: the detection of complex drift dynamic and
the assessment of business process resilience.
Keywords
Process Mining, Process Dynamics, Multi-Perspective Analysis, Concept Drifts, Process Resilience
1. Introduction
Process mining is an emerging research discipline that provides techniques to analyze and
improve processes in different application domains [1]. Traditionally, process mining considers
processes as static, unchanging systems over time. However, in reality business processes are
subject to frequent changes due to the dynamic environment in which organizations operate.
For many process mining problems, it is important to account for these changes by analyzing
business processes as dynamic systems in order to recognize when and how they change over
time. Some process mining techniques already investigate such process dynamics when it
comes, for instance, to process simulation or process prediction [2, 3]. Nevertheless, there still
exists enormous research potential in this direction, especially, in a multi-perspective manner,
when process analysis goes beyond the control-flow and considers additional event attributes
to analyze processes from other perspectives (time, resource, data). Therefore, the research
objective of the PhD thesis is to develop multi-perspective analysis approaches that consider
a business process as a dynamic system and provide valuable insights into its overall evolution.
The remainder presents two ongoing projects. In the first one, we focus on the detection
of past process changes to reveal complex drift dynamics, resulting in a better understanding of
the overall process evolution. In the second one, we consider sudden process changes (shocks)
and estimate their impact on process performance to measure business process resilience. The
scope of the PhD thesis will be extended with another research direction in the future.
ICPM 2022 Doctoral Consortium and Tool Demonstration Track
Envelope-Open alexander.kraus@uni-mannheim.de (A. Kraus)
© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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Workshop
Proceedings
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8
2. Detection of Complex Drift Dynamics
Business processes are widely supported by information systems, which record process
execution in the form of event logs. The event logs represent snapshots of data generated over
a specific period of time. Due to their dynamic environments, business processes under analysis
are often not in a steady state, but are rather subject to frequent change [4]. These changes
can result in the presence of concept drifts in event logs. Concept drift describes a situation
when a business process changes while being analyzed [4], resulting in several process versions.
To avoid polluting process mining results by mixing up these different versions, concept drift
detection strives to identify them [4].
Recognizing the detrimental impact that such concept drifts can have on obtained process
mining results, many techniques have been proposed to detect concept drifts from event
logs [5, 6]. Foremost, this involves the detection of simple drifts that consist of a single change.
Single changes are then characterized in terms of their moment, type (sudden vs. gradual),
region of change, and process perspective. However, in many cases, process changes do not
happen in isolation. Rather, the evolution of a process can manifest itself through what we refer
to as complex drifts. Complex drifts consist of multiple, related process changes. Well-known
examples of complex drifts are recurring and incremental drifts [7], which each consists of a
series of connected changes, and multi-order drifts, which occur when multiple drifts coincide,
typically on different time scales (such as monthly and quarterly recurring changes) [8]. Many
other forms of complex drifts are possible. For instance, Figure 1 illustrates an example of
complex drift, where an incremental drift consists of several sudden and gradual process changes.
gradual
Process versions
gradual sudden
gradual sudden
Time
Figure 1: Illustrative example of complex drift, showing a sequence of sudden and gradual process
changes that form a long incremental drift. Since gradual changes are not considered as parts of an
incremental drift, existing techniques fail to recognize the actual drift dynamics, resulting in a mix
of gradual and sudden drifts. To obtain complete insights into the evolution of a process over time,
we aim to develop an approach that can detect these kinds of drift dynamics.
Motivation. Despite the relevance of complex drifts, most techniques just focus on the
identification of simple drifts [5]. Some techniques can detect incremental and recurring
drifts [9, 10] or basic multi-order drifts [8]. However, their scope does not address all kinds
of complex drifts and change types collectively, as shown in Table 1, resulting in incomplete
insights into the evolution of a process over time. For instance, existing techniques fail to
recognize the actual drift dynamics in Figure 1. They detect a mix of gradual and sudden
drifts since gradual changes are not considered as parts of an incremental drift. Furthermore,
the usability of these techniques is limited because the detection of drifts is semi-automated
9
and requires various input parameters that are unknown upfront. As a result, the problem
of comprehensively detecting complex drifts is far from addressed.
Research goal. The research goal of the project is to develop an approach that can detect all
complex drifts and process change types, as shown in Table 1. Comprehensive detection of com-
plex drift dynamics requires identification of process changes and their relations from different
process perspectives. As a result, we get a better understanding of how a process really changed.
Table 1
The scope of our approach vs. existing state-of-the-art techniques. Comprehensive detection of complex
drifts requires an approach that can detect all complex drifts and process change types under one
umbrella. Otherwise, obtained concept drift detection results might not reveal the actual drift dynamics.
Complex drifts
Change type
Incremental Recurring Multi-order
Sudden [9, 10] [8]
Gradual Our scope
Approach idea. We first divide an event log into a series of ordered, non-overlapping event
windows, where each window contains events that are observed during a particular period.
Then, for each window, we calculate system-level process characteristics that describe a process
from multiple perspectives. These characteristics are represented in the form of distributions.
The obtained distributions are compared with each other, resulting in a similarity matrix.
Finally, the similarity matrix is used to create a cluster hierarchy. Based on the obtained cluster
hierarchy, the approach detects process changes and connects them to drifts at different levels
of time granularity using measures for process similarity and drift severity.
Evaluation. We evaluate our approach with the objective to demonstrate its accuracy and use-
fulness. To that end, we first plan to use synthetic event logs, generated using the CDLG tool [11],
to show that our approach can accurately detect complex drifts. Second, we aim to conduct
case studies using real-life logs that are known to have concept drifts. We compare our findings
with the state-of-the-art techniques [12, 13, 14] and discuss the obtained additional insights.
3. Assessment of Business Process Resilience
Resilience is understood as a company’s ability to, and speed at which it can return to its
normal performance level following a disruptive event [15]. Given the increasing amount of
disruptive events, like natural disasters, pandemic disease, economic recession, equipment
failure, and human errors, resilience becomes a key competence of a company for success and
survival in today’s turbulent business environment [16]. If organizations can assess resilience,
they can take temporary or structural countermeasures to improve their resilience, ensuring
that their operations keep running smoothly in light of sudden disruptive changes.
Motivation. Although the awareness for process resilience is the first important step towards
the overall improvement of resilience [17], the problem of automated and data-driven
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assessment of business process resilience remains unresolved. Existing works provide a support
framework at a conceptual level [18] or focus on achieving resilience during the process
design phase [19]. Existing data-driven procedure to measure process resilience [20] is based
on manual creation of a simulation model and has a limited scope, i.e., it only considers the
average lead time as the process performance indicator (PPI) and the changes in the arrival
rate as the only disruptive scenario. Furthermore, a significant amount of simulations is needed
to obtain statistically reliable results.
Research goal. To overcome the existing limitations with respect to the scope and usability,
we propose a novel data-driven approach to measure process resilience. As depicted in Figure 2,
for a given PPI and a disruptive event, our assessment provides insights into four different
aspects of process resilience: the expected impact delay, performance drop, recovery time, and
the total performance loss. The resilience assessment can be conducted with respect to different
PPIs and disruptive events. Our approach is automated and does not require any additional
information or manual work.
2. Performance drop
Process performance
indicator (PPI)
4. Total
performance loss
1. Impact
delay
3. Recovery time Normal
Disruptive event PPI level
Time
Figure 2: Process resilience concept based on PPI’s deviation from the normal level after a disruptive
event. Using our approach, we can automatically quantify resilience through its four main measures:
impact delay, maximal performance drop, recovery time, and total performance loss. Our approach
only uses an event log and does not require manual creation of a simulation model. Its scope allows
assessing process resilience with respect to different PPIs and disruptive events.
Approach idea. We consider the input-output dynamics of a process using system-level
process characteristics that cover different process perspectives. These process characteristics
are represented in the form of time series. We use these time series to create a collection of
vector autoregression models [21]. From the collection, we select the best model and conduct
impulse-response analysis. This analysis provides insights about the four main resilience aspects.
Evaluation. In our evaluation, we plan to demonstrate our approach’s capability to obtain
insights about process resilience in a realistic scenario. To achieve this, we evaluate our
approach by employing it on a set of real-world event logs that record executions of the same
business process at five different organizations (BPI-15 challenge). For each organization, we
consider different types of disruptive events and compare the obtained resilience insights.
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