A visual approach to spot statistically-significant differences in event logs based on process metrics

A.J. Bolt Iriondo; M. De Leoni; W.M.P. Van Der Aalst

doi:10.1007/978-3-319-39696-5.10

A visual approach to spot statistically-significant differences in event logs based on process metrics

A.J. Bolt Iriondo, M. De Leoni, W.M.P. Van Der Aalst

Process Analytics

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

38 Citations (Scopus)

3 Downloads (Pure)

Abstract

This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the variants. The results are visualized as transitions systems, which are colored to pinpoint the significant differences. The approach has been implemented in ProM, and the implementation is publicly available. We validated our approach by performing experiments using real-life event data. The results show how our technique is able to detect relevant differences undetected by previous approaches while it avoids detecting insignificant differences.

Original language	English
Title of host publication	Advanced Information Systems Engineering
Subtitle of host publication	28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings
Editors	S. Nurcan, P. Soffer, M. Bajec, J. Eder
Place of Publication	Dordrecht
Publisher	Springer
Pages	151-166
Number of pages	16
ISBN (Electronic)	978-3-319-39696-5
ISBN (Print)	978-3-319-39695-8
DOIs	https://doi.org/10.1007/978-3-319-39696-5.10
Publication status	Published - 2016
Event	28th International Conference on Advanced Information Systems Engineering (CAiSE 2016) - Ljubljana, Slovenia Duration: 13 Jun 2016 → 17 Jun 2016 Conference number: 28 http://caise2016.si/

Publication series

Name	Lecture Notes in Computer Science
Volume	9694
ISSN (Print)	03029743
ISSN (Electronic)	16113349

Conference

Conference	28th International Conference on Advanced Information Systems Engineering (CAiSE 2016)
Abbreviated title	CAiSE '16
Country/Territory	Slovenia
City	Ljubljana
Period	13/06/16 → 17/06/16
Other	"Information Systems for Connecting People"
Internet address	http://caise2016.si/

Keywords

Annotated transition system
Process mining
Process variants comparison
Statistical significance

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10.1007/978-3-319-39696-5.10

Cite this

Bolt Iriondo, A. J., De Leoni, M., & Van Der Aalst, W. M. P. (2016). A visual approach to spot statistically-significant differences in event logs based on process metrics. In S. Nurcan, P. Soffer, M. Bajec, & J. Eder (Eds.), Advanced Information Systems Engineering: 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings (pp. 151-166). (Lecture Notes in Computer Science; Vol. 9694). Springer. https://doi.org/10.1007/978-3-319-39696-5.10

Bolt Iriondo, A.J. ; De Leoni, M. ; Van Der Aalst, W.M.P. / A visual approach to spot statistically-significant differences in event logs based on process metrics. Advanced Information Systems Engineering: 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. editor / S. Nurcan ; P. Soffer ; M. Bajec ; J. Eder. Dordrecht : Springer, 2016. pp. 151-166 (Lecture Notes in Computer Science).

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abstract = "This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the variants. The results are visualized as transitions systems, which are colored to pinpoint the significant differences. The approach has been implemented in ProM, and the implementation is publicly available. We validated our approach by performing experiments using real-life event data. The results show how our technique is able to detect relevant differences undetected by previous approaches while it avoids detecting insignificant differences.",

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Bolt Iriondo, AJ, De Leoni, M & Van Der Aalst, WMP 2016, A visual approach to spot statistically-significant differences in event logs based on process metrics. in S Nurcan, P Soffer, M Bajec & J Eder (eds), Advanced Information Systems Engineering: 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. Lecture Notes in Computer Science, vol. 9694, Springer, Dordrecht, pp. 151-166, 28th International Conference on Advanced Information Systems Engineering (CAiSE 2016), Ljubljana, Slovenia, 13/06/16. https://doi.org/10.1007/978-3-319-39696-5.10

A visual approach to spot statistically-significant differences in event logs based on process metrics. / Bolt Iriondo, A.J.; De Leoni, M.; Van Der Aalst, W.M.P.
Advanced Information Systems Engineering: 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. ed. / S. Nurcan; P. Soffer; M. Bajec; J. Eder. Dordrecht: Springer, 2016. p. 151-166 (Lecture Notes in Computer Science; Vol. 9694).

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

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AB - This paper addresses the problem of comparing different variants of the same process. We aim to detect relevant differences between processes based on what was recorded in event logs. We use transition systems to model behavior and to highlight differences. Transition systems are annotated with measurements, used to compare the behavior in the variants. The results are visualized as transitions systems, which are colored to pinpoint the significant differences. The approach has been implemented in ProM, and the implementation is publicly available. We validated our approach by performing experiments using real-life event data. The results show how our technique is able to detect relevant differences undetected by previous approaches while it avoids detecting insignificant differences.

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Bolt Iriondo AJ, De Leoni M, Van Der Aalst WMP. A visual approach to spot statistically-significant differences in event logs based on process metrics. In Nurcan S, Soffer P, Bajec M, Eder J, editors, Advanced Information Systems Engineering: 28th International Conference, CAiSE 2016, Ljubljana, Slovenia, June 13-17, 2016. Proceedings. Dordrecht: Springer. 2016. p. 151-166. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-39696-5.10

A visual approach to spot statistically-significant differences in event logs based on process metrics

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