Data-aware process mining : discovering decisions in processes using alignments

M. Leoni, de; W.M.P. Aalst, van der

doi:10.1145/2480362.2480633

Data-aware process mining : discovering decisions in processes using alignments

M. Leoni, de, W.M.P. Aalst, van der

Process Science

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

110 Citaten (Scopus)

7 Downloads (Pure)

Samenvatting

Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.

Originele taal-2	Engels
Titel	28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013)
Plaats van productie	New York NY
Uitgeverij	Association for Computing Machinery, Inc
Pagina's	1454-1461
ISBN van geprinte versie	978-1-4503-1656-9
DOI's	https://doi.org/10.1145/2480362.2480633
Status	Gepubliceerd - 2013
Evenement	28th ACM Symposium on Applied Computing (SAC 2013) - Coimbra, Portugal Duur: 18 mrt 2013 → 22 mrt 2013 Congresnummer: 28

Congres

Congres	28th ACM Symposium on Applied Computing (SAC 2013)
Verkorte titel	SAC 2013
Land/Regio	Portugal
Stad	Coimbra
Periode	18/03/13 → 22/03/13
Ander	28th Annual ACM Symposium on Applied Computing

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@inproceedings{2f33e09f73c54f49a9798e39f8357f3a,

title = "Data-aware process mining : discovering decisions in processes using alignments",

abstract = "Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.",

author = "{Leoni, de}, M. and {Aalst, van der}, W.M.P.",

year = "2013",

doi = "10.1145/2480362.2480633",

language = "English",

isbn = "978-1-4503-1656-9",

pages = "1454--1461",

booktitle = "28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013)",

publisher = "Association for Computing Machinery, Inc",

address = "United States",

note = "28th ACM Symposium on Applied Computing (SAC 2013), SAC 2013 ; Conference date: 18-03-2013 Through 22-03-2013",

}

Leoni, de, M & Aalst, van der, WMP 2013, Data-aware process mining : discovering decisions in processes using alignments. in 28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013). Association for Computing Machinery, Inc, New York NY, blz. 1454-1461, 28th ACM Symposium on Applied Computing (SAC 2013), Coimbra, Portugal, 18/03/13. https://doi.org/10.1145/2480362.2480633

Data-aware process mining : discovering decisions in processes using alignments. / Leoni, de, M.; Aalst, van der, W.M.P.

28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013). New York NY : Association for Computing Machinery, Inc, 2013. blz. 1454-1461.

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

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N2 - Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.

AB - Process discovery, i.e., learning process models from event logs, has attracted the attention of researchers and practitioners. Today, there exists a wide variety of process mining techniques that are able to discover the control-flow of a process based on event data. These techniques are able to identify decision points, but do not analyze data flow to find rules explaining why individual cases take a particular path. Fortunately, recent advances in conformance checking can be used to align an event log with data and a process model with decision points. These alignments can be used to generate a well-defined classification problem per decision point. This way data flow and guards can be discovered and added to the process model.

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DO - 10.1145/2480362.2480633

M3 - Conference contribution

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BT - 28th Annual ACM Symposium on Applied Computing (Coimbra, Portugal, March 18-22, 2013)

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Y2 - 18 March 2013 through 22 March 2013

ER -

Data-aware process mining : discovering decisions in processes using alignments

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