Discovering and navigating a collection of process models using multiple quality dimensions

J.C.A.M. Buijs; B.F. Dongen, van; W.M.P. Aalst, van der

doi:10.1007/978-3-319-06257-0_1

Discovering and navigating a collection of process models using multiple quality dimensions

J.C.A.M. Buijs, B.F. Dongen, van, W.M.P. Aalst, van der

Process Analytics

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

7 Citations (Scopus)

Abstract

Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of mutually non-dominating process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different trade-offs between different quality dimensions. Keywords: Process mining; Process model quality; Process model collection

Original language	English
Title of host publication	Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers)
Editors	N. Lohmann, M. Song, P. Wohed
Place of Publication	Berlin
Publisher	Springer
Pages	3-14
ISBN (Print)	978-3-319-06256-3
DOIs	https://doi.org/10.1007/978-3-319-06257-0_1
Publication status	Published - 2014
Event	9th International Workshop on Business Process Intelligence (BPI 2013) - Beijing, China Duration: 26 Aug 2013 → 26 Aug 2013 Conference number: 9

Publication series

Name	Lecture Notes in Business Information Processing
Volume	171
ISSN (Print)	1865-1348

Workshop

Workshop	9th International Workshop on Business Process Intelligence (BPI 2013)
Abbreviated title	BPI 2013
Country/Territory	China
City	Beijing
Period	26/08/13 → 26/08/13
Other	Workshop held in conjunction with the 11th International Conference on Business Process Management (BPM 2013)

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10.1007/978-3-319-06257-0_1

Cite this

Buijs, J. C. A. M., Dongen, van, B. F., & Aalst, van der, W. M. P. (2014). Discovering and navigating a collection of process models using multiple quality dimensions. In N. Lohmann, M. Song, & P. Wohed (Eds.), Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers) (pp. 3-14). (Lecture Notes in Business Information Processing; Vol. 171). Springer. https://doi.org/10.1007/978-3-319-06257-0_1

Buijs, J.C.A.M. ; Dongen, van, B.F. ; Aalst, van der, W.M.P. / Discovering and navigating a collection of process models using multiple quality dimensions. Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers). editor / N. Lohmann ; M. Song ; P. Wohed. Berlin : Springer, 2014. pp. 3-14 (Lecture Notes in Business Information Processing).

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title = "Discovering and navigating a collection of process models using multiple quality dimensions",

abstract = "Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of mutually non-dominating process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different trade-offs between different quality dimensions. Keywords: Process mining; Process model quality; Process model collection",

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Buijs, JCAM, Dongen, van, BF & Aalst, van der, WMP 2014, Discovering and navigating a collection of process models using multiple quality dimensions. in N Lohmann, M Song & P Wohed (eds), Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers). Lecture Notes in Business Information Processing, vol. 171, Springer, Berlin, pp. 3-14, 9th International Workshop on Business Process Intelligence (BPI 2013), Beijing, China, 26/08/13. https://doi.org/10.1007/978-3-319-06257-0_1

Discovering and navigating a collection of process models using multiple quality dimensions. / Buijs, J.C.A.M.; Dongen, van, B.F.; Aalst, van der, W.M.P.
Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers). ed. / N. Lohmann; M. Song; P. Wohed. Berlin: Springer, 2014. p. 3-14 (Lecture Notes in Business Information Processing; Vol. 171).

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

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AB - Process discovery algorithms typically aim at discovering a process model from an event log that best describes the recorded behavior. However, multiple quality dimensions can be used to evaluate a process model. In previous work we showed that there often is not one single process model that describes the observed behavior best in all quality dimensions. Therefore, we present an extension to our flexible ETM algorithm that does not result in a single best process model but in a collection of mutually non-dominating process models. This is achieved by constructing a Pareto front of process models. We show by applying our approach on a real life event log that the resulting collection of process models indeed contains several good candidates. Furthermore, by presenting a collection of process models, we show that it allows the user to investigate the different trade-offs between different quality dimensions. Keywords: Process mining; Process model quality; Process model collection

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Buijs JCAM, Dongen, van BF, Aalst, van der WMP. Discovering and navigating a collection of process models using multiple quality dimensions. In Lohmann N, Song M, Wohed P, editors, Business Process Management Workshops (BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers). Berlin: Springer. 2014. p. 3-14. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-319-06257-0_1

Discovering and navigating a collection of process models using multiple quality dimensions

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