Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/32722
Title: Unsupervised Event Abstraction in a Process Mining Context: A Benchmark Study
Authors: VAN HOUDT, Greg 
DEPAIRE, Benoit 
MARTIN, Niels 
Issue Date: 2021
Publisher: Springer
Source: Leemans, S.; Leopold, H. (Eds.). Process Mining Workshops ICPM 2020 International Workshops, Padua, Italy, October 5–8, 2020, Revised Selected Papers, p. 82-93.
Series/Report: Lecture Notes in Business Information Processing
Series/Report no.: 406
Abstract: Due to the rise of IoT, event data becomes increasingly fine-grained. Faced with such data, process discovery often produces incomprehensible spaghetti-models expressed at a granularity level that doesn't match the mental model of a business user. One approach is to use event abstraction patterns to transform the event log towards a more coarse-grained level and to discover process models from this transformed log. Recent literature has produced various (partial) implementations of this approach, but insights how these techniques compare against each other is still limited. This paper focuses on the use of Local Process Models and Combination based Behavioural Pattern Mining to discover event abstraction patterns in combination with the approach of Mannhardt et al. to transform the event log. Experiments are conducted to gain insights into the performance of these techniques. Results are very limited with a general decrease in fitness and precision and only a minimal improvement of complexity. Results also show that the combination of the process discovery algorithm and the event abstraction pattern miner matters. In particular, the combination of Local Process Models with Split Miner seems to improve precision.
Keywords: Process Mining;Unsupervised Learning;Event Log;Abstraction
Document URI: http://hdl.handle.net/1942/32722
ISBN: 9783030726928
DOI: 10.1007/978-3-030-72693-5_7
Category: C1
Type: Proceedings Paper
Appears in Collections:Research publications

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