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 |
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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 | |
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 |
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Period | 1/01/10 → … |
Other | 2010 IEEE World Congress on Computational Intelligence |