Discovering process models with genetic algorithms using sampling

Research output: Chapter in Book/Report/Conference proceedingChapterAcademic

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Abstract

Process mining, a new business intelligence area, aims at discovering process models from event logs. Complex constructs, noise and infrequent behavior are issues that make process mining a complex problem. A genetic mining algorithm, which applies genetic operators to search in the space of all possible process models, deals with the aforementioned challenges with success. Its drawback is high computation time due to the high time costs of the fitness evaluation. Fitness evaluation time linearly depends on the number of process instances in the log. By using a sampling-based approach, i.e. evaluating fitness on a sample from the log instead of the whole log, we drastically reduce the computation time. When the desired fitness is achieved on the sample, we check the fitness on the whole log; if it is not achieved yet, we increase the sample size and continue the computation iteratively. Our experiments show that sampling works well even for relatively small logs, and the total computation time is reduced by 6 up to 15 times.
Original languageEnglish
Title of host publicationKnowledge-Based and Intelligent Information and Engineering Systems (14th International Conference, KES'2010, Cardiff, UK, September 8-10, 2010. Proceedings)
EditorsR. Setchi, I. Jordanov, R.J. Howlett, L.C. Jain
Place of PublicationBerlin
PublisherSpringer
Pages41-50
ISBN (Print)978-3-642-15386-0
DOIs
Publication statusPublished - 2010
Eventconference; 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'2010); 2010-09-08; 2010-09-10 -
Duration: 8 Sep 201010 Sep 2010

Publication series

NameLecture Notes in Computer Science
Volume6276

Conference

Conferenceconference; 14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'2010); 2010-09-08; 2010-09-10
Period8/09/1010/09/10
Other14th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES'2010)

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