Control-flow discovery from event streams

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

doi:10.1109/CEC.2014.6900341

Control-flow discovery from event streams

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

Process Analytics

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

69 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	IEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014)
Place of Publication	Piscataway
Publisher	Institute of Electrical and Electronics Engineers
Pages	2420-2427
ISBN (Print)	978-1-4799-6626-4
DOIs	https://doi.org/10.1109/CEC.2014.6900341
Publication status	Published - 2014
Event	conference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11 - Duration: 6 Jul 2014 → 11 Jul 2014

Conference

Conference	conference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11
Period	6/07/14 → 11/07/14
Other	2014 IEEE Congress on Evolutionary Computation

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10.1109/CEC.2014.6900341

Cite this

@inproceedings{3677dc7c62f948c885ee9475728103b7,

title = "Control-flow discovery from event streams",

abstract = "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.",

author = "A. Burattin and A. Sperduti and {Aalst, van der}, W.M.P.",

year = "2014",

doi = "10.1109/CEC.2014.6900341",

language = "English",

isbn = "978-1-4799-6626-4",

pages = "2420--2427",

booktitle = "IEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014)",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "conference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11 ; Conference date: 06-07-2014 Through 11-07-2014",

}

Burattin, A, Sperduti, A & Aalst, van der, WMP 2014, Control-flow discovery from event streams. in IEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014). Institute of Electrical and Electronics Engineers, Piscataway, pp. 2420-2427, conference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11, 6/07/14. https://doi.org/10.1109/CEC.2014.6900341

Control-flow discovery from event streams. / Burattin, A.; Sperduti, A.; Aalst, van der, W.M.P.
IEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014). Piscataway: Institute of Electrical and Electronics Engineers, 2014. p. 2420-2427.

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

TY - GEN

T1 - Control-flow discovery from event streams

AU - Burattin, A.

AU - Sperduti, A.

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

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1109/CEC.2014.6900341

DO - 10.1109/CEC.2014.6900341

M3 - Conference contribution

SN - 978-1-4799-6626-4

SP - 2420

EP - 2427

BT - IEEE Congress on Evolutionary Computation (CEC'14, Beijing, China, July 6-11, 2014)

PB - Institute of Electrical and Electronics Engineers

CY - Piscataway

T2 - conference; 2014 IEEE Congress on Evolutionary Computation; 2014-07-06; 2014-07-11

Y2 - 6 July 2014 through 11 July 2014

ER -

Control-flow discovery from event streams

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