Control-flow discovery from event streams

A. Burattin, A. Sperduti, W.M.P. Aalst, van der

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

54 Citaten (Scopus)


Process Mining represents an important research field that connects Business Process Modeling and Data Mining. One of the most prominent task of Process Mining is the discovery of a control-flow starting from event logs. This paper focuses on the important problem of control-flow discovery starting from a stream of event data. We propose to adapt Heuristics Miner, one of the most effective control-flow discovery algorithms, to the treatment of streams of event data. Two adaptations, based on Lossy Counting and Lossy Counting with Budget, as well as a sliding window based version of Heuristics Miner, are proposed and experimentally compared against both artificial and real streams. Experimental results show the effectiveness of control-flow discovery algorithms for streams on artificial and real datasets.
Originele taal-2Engels
TitelIEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014)
Plaats van productiePiscataway
UitgeverijInstitute of Electrical and Electronics Engineers
ISBN van geprinte versie978-1-4799-6626-4
StatusGepubliceerd - 2014
Evenementconference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11 -
Duur: 6 jul 201411 jul 2014


Congresconference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11
Ander2014 IEEE Congress on Evolutionary Computation


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