Using domain knowledge to enhance process mining results

P.M. Dixit; J.C.A.M. Buijs; W.M.P. van der Aalst; B.F.A. Hompes; J. Buurman

doi:10.1007/978-3-319-53435-0_4

Using domain knowledge to enhance process mining results

P.M. Dixit
, J.C.A.M. Buijs
, W.M.P. van der Aalst
, B.F.A. Hompes
, J. Buurman

Process Analytics

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

18 Citaten (Scopus)

3 Downloads (Pure)

Samenvatting

Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.

Originele taal-2	Engels
Titel	Data-Driven Process Discovery and Analysis
Subtitel	5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers
Redacteuren	P. Ceravolo, S. Rinderle-Ma
Plaats van productie	Dordrecht
Uitgeverij	Springer
Pagina's	76-104
Aantal pagina's	29
ISBN van elektronische versie	978-3-319-53435-0
ISBN van geprinte versie	978-3-319-53434-3
DOI's	https://doi.org/10.1007/978-3-319-53435-0_4
Status	Gepubliceerd - 2017
Evenement	5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015 - Vienna, Oostenrijk Duur: 9 dec. 2015 → 11 dec. 2015 Congresnummer: 5

Publicatie series

Naam	Lecture Notes in Business Information Processing
Volume	244
ISSN van geprinte versie	18651348

Congres

Congres	5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015
Verkorte titel	SIMPDA 2015
Land/Regio	Oostenrijk
Stad	Vienna
Periode	9/12/15 → 11/12/15

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10.1007/978-3-319-53435-0_4

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Dixit, P. M., Buijs, J. C. A. M., van der Aalst, W. M. P., Hompes, B. F. A., & Buurman, J. (2017). Using domain knowledge to enhance process mining results. In P. Ceravolo, & S. Rinderle-Ma (editors), Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers (blz. 76-104). (Lecture Notes in Business Information Processing; Vol. 244). Springer. https://doi.org/10.1007/978-3-319-53435-0_4

Dixit, P.M. ; Buijs, J.C.A.M. ; van der Aalst, W.M.P. et al. / Using domain knowledge to enhance process mining results. Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. redacteur / P. Ceravolo ; S. Rinderle-Ma . Dordrecht : Springer, 2017. blz. 76-104 (Lecture Notes in Business Information Processing).

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title = "Using domain knowledge to enhance process mining results",

abstract = "Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.",

keywords = "Algorithm post processing, Declare templates, Domain knowledge, User guided process discovery",

author = "P.M. Dixit and J.C.A.M. Buijs and \{van der Aalst\}, W.M.P. and B.F.A. Hompes and J. Buurman",

year = "2017",

doi = "10.1007/978-3-319-53435-0\_4",

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booktitle = "Data-Driven Process Discovery and Analysis",

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Dixit, PM, Buijs, JCAM, van der Aalst, WMP, Hompes, BFA & Buurman, J 2017, Using domain knowledge to enhance process mining results. in P Ceravolo & S Rinderle-Ma (reds.), Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. Lecture Notes in Business Information Processing, vol. 244, Springer, Dordrecht, blz. 76-104, 5th International Symposium on Data-Driven Process Discovery and Analysis (SIMPDA 2015, Vienna, Oostenrijk, 9/12/15. https://doi.org/10.1007/978-3-319-53435-0_4

Using domain knowledge to enhance process mining results. / Dixit, P.M.; Buijs, J.C.A.M.; van der Aalst, W.M.P. et al.
Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. reds. / P. Ceravolo; S. Rinderle-Ma . Dordrecht: Springer, 2017. blz. 76-104 (Lecture Notes in Business Information Processing; Vol. 244).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - Buurman, J.

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AB - Process discovery algorithms typically aim at discovering process models from event logs. Most algorithms achieve this by solely using an event log, without allowing the domain expert to influence the discovery in any way. However, the user may have certain domain expertise which should be exploited to create better process models. In this paper, we address this issue of incorporating domain knowledge to improve the discovered process model. First, we present a verification algorithm to verify the presence of certain constraints in a process model. Then, we present three modification algorithms to modify the process model. The outcome of our approach is a Pareto front of process models based on the constraints specified by the domain expert and common quality dimensions of process mining.

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Dixit PM, Buijs JCAM, van der Aalst WMP, Hompes BFA, Buurman J. Using domain knowledge to enhance process mining results. In Ceravolo P, Rinderle-Ma S, redacteurs, Data-Driven Process Discovery and Analysis : 5th IFIP WG 2.6 International Symposium, SIMPDA 2015, Vienna, Austria, December 9-11, 2015, Revised Selected Papers. Dordrecht: Springer. 2017. blz. 76-104. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-319-53435-0_4

Using domain knowledge to enhance process mining results

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