Distributed genetic process mining

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

20 Citations (Scopus)

Abstract

Process mining aims at discovering process models from data logs in order to offer insight into the real use of information systems. Most of the existing process mining algorithms fail to discover complex constructs or have problems dealing with noise and infrequent behavior. The genetic process mining algorithm overcomes these issues by using genetic operators to search for the fittest solution in the space of all possible process models. The main disadvantage of genetic process mining is the required computation time. In this paper we present a coarse-grained distributed variant of the genetic miner that reduces the computation time. The degree of the improvement obtained highly depends on the parameter values and event logs characteristics. We perform an empirical evaluation to determine guidelines for setting the parameters of the distributed algorithm.
Original languageEnglish
Title of host publicationProceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010)
PublisherInstitute of Electrical and Electronics Engineers
Pages1951-1958
ISBN (Print)978-1-4244-6909-3
DOIs
Publication statusPublished - 2010
Eventconference; 2010 IEEE World Congress on Computational Intelligence -
Duration: 1 Jan 2010 → …

Conference

Conferenceconference; 2010 IEEE World Congress on Computational Intelligence
Period1/01/10 → …
Other2010 IEEE World Congress on Computational Intelligence

Fingerprint

Dive into the research topics of 'Distributed genetic process mining'. Together they form a unique fingerprint.

Cite this