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A Generic Framework for Attribute-Driven Hierarchical Trace Clustering

 
: Zelst, S.J. van; Cao, Y.

:

Río Ortega, A. del:
Business Process Management Workshops. BPM 2020 International Workshops : Seville, Spain, September 13-18, 2020, Revised Selected Papers
Cham: Springer Nature, 2020 (Lecture Notes in Business Information Processing 397)
ISBN: 978-3-030-66497-8 (Print)
ISBN: 978-3-030-66498-5 (Online)
ISBN: 978-3-030-66499-2
S.308-320
International Conference on Business Process Management (BPM) <18, 2020, Online>
International Workshop on Business Process Intelligence (BPI) <16, 2020, Online>
Englisch
Konferenzbeitrag
Fraunhofer FIT ()

Abstract
The execution of business processes often entails a specific process execution context, e.g. a customer, service or product. Often, the corresponding event data logs indicators of such an execution context, e.g., a customer type (bronze, silver, gold or platinum). Typically, variations in the execution of a process exist for the different execution context of a process. To gain a better understanding of the global process execution, it is interesting to study the behavioral (dis)similarity between different execution contexts of a process. However, in real business settings, the exact number of execution contexts might be too large to analyze manually. At the same time, current trace clustering techniques do not take process type information into account, i.e., they are solely behaviorally driven. Hence, in this paper, we present a hierarchical data-attribute-driven trace clustering framework that allows us to compare the behavior of different groups of traces. Our evaluation shows that the incorporation of data-attributes in trace clustering yields interesting novel process insights.

: http://publica.fraunhofer.de/dokumente/N-639221.html