Keynote: Data Mining on Process Data

MCML Authors

Thomas Seidl

Prof. Dr.

Director

Database Systems, Data Mining and AI

Abstract

Data Mining and Process Mining -- is one just a variant of the other, or do worlds separate the two areas from each other? The notions sound so similar but the contents sometimes look differently, so respective researchers may get confused in their mutual perception, be it authors or reviewers. The talk recalls commonalities like model-based supervised and unsupervised learning approaches, and it also sheds light to peculiarities in process data and process mining tasks as seen from a data mining perspective. When considering trace data from event log files as time series, as sequences, or as activity sets, quite different data mining techniques apply and may be extended and improved. A particular example is rare pattern mining, which fills a gap between frequent patterns and outlier detection. The task aims at identifying patterns that occur with low frequency but above single outliers. Structural deficiences may cause malfunctions or other undesired behavior which get discarded as outliers in event logs, since they are observed infrequently only. Rare pattern mining may identify these situations, and recent approaches include clustering or ordering non-conformant traces. The talk concludes with some remarks on how to sell process mining papers to the data mining community, and vice versa, in order to improve mutual acceptance, and to increase synergies in the fields.

inproceedings

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ICPM 2020

2nd International Conference on Process Mining. Virtual, Oct 04-09, 2020.

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Authors

T. Seidl

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Links

DOI

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Research Area

 A3 | Computational Models