Distributed genetic process mining

C.C. Bratosin; N. Sidorova; W.M.P. Aalst, van der

doi:10.1109/CEC.2010.5586250

Distributed genetic process mining

C.C. Bratosin, N. Sidorova, W.M.P. Aalst, van der

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

22 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 language	English
Title of host publication	Proceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010)
Publisher	Institute of Electrical and Electronics Engineers
Pages	1951-1958
ISBN (Print)	978-1-4244-6909-3
DOIs	https://doi.org/10.1109/CEC.2010.5586250
Publication status	Published - 2010
Event	conference; 2010 IEEE World Congress on Computational Intelligence - Duration: 1 Jan 2010 → …

Conference

Conference	conference; 2010 IEEE World Congress on Computational Intelligence
Period	1/01/10 → …
Other	2010 IEEE World Congress on Computational Intelligence

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10.1109/CEC.2010.5586250

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title = "Distributed genetic process mining",

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.",

author = "C.C. Bratosin and N. Sidorova and {Aalst, van der}, W.M.P.",

year = "2010",

doi = "10.1109/CEC.2010.5586250",

language = "English",

isbn = "978-1-4244-6909-3",

pages = "1951--1958",

booktitle = "Proceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "conference; 2010 IEEE World Congress on Computational Intelligence ; Conference date: 01-01-2010",

}

Bratosin, CC, Sidorova, N & Aalst, van der, WMP 2010, Distributed genetic process mining. in Proceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010). Institute of Electrical and Electronics Engineers, pp. 1951-1958, conference; 2010 IEEE World Congress on Computational Intelligence, 1/01/10. https://doi.org/10.1109/CEC.2010.5586250

Distributed genetic process mining. / Bratosin, C.C.; Sidorova, N.; Aalst, van der, W.M.P.
Proceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010). Institute of Electrical and Electronics Engineers, 2010. p. 1951-1958.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

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AU - Sidorova, N.

AU - Aalst, van der, W.M.P.

PY - 2010

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N2 - 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.

AB - 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.

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BT - Proceedings 2010 IEEE World Congress on Computational Intelligence (IEEE CEC 2010, Barcelona, Spain, July 18-23, 2010)

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Distributed genetic process mining

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