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Log-based simplification of process models

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Log-based simplification of process models
Συγγραφείς: San Pedro Martín, Javier de, Carmona Vargas, Josep, Cortadella, Jordi
Συνεισφορές: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació, Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
Στοιχεία εκδότη: Springer
Έτος έκδοσης: 2015
Συλλογή: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
Θεματικοί όροι: Àrees temàtiques de la UPC::Informàtica::Sistemes d'informació, Business process modeling, Systems engineering--Computer simulation, Enginyeria de sistemes -- Models matemàtics
Περιγραφή: The visualization of models is essential for user-friendly human-machine interactions during Process Mining. A simple graphical representation contributes to give intuitive information about the behavior of a system. However, complex systems cannot always be represented with succinct models that can be easily visualized. Quality-preserving model simplifications can be of paramount importance to alleviate the complexity of finding useful and attractive visualizations. This paper presents a collection of log-based techniques to simplify process models. The techniques trade off visual-friendly properties with quality metrics related to logs, such as fitness and precision, to avoid degrading the resulting model. The algorithms, either cast as optimization problems or heuristically guided, find simplified versions of the initial process model, and can be applied in the final stage of the process mining life-cycle, between the discovery of a process model and the deployment to the final user. A tool has been developed and tested on large logs, producing simplified process models that are one order of magnitude smaller while keeping fitness and precision under reasonable margins. ; Peer Reviewed ; Postprint (author's final draft)
Τύπος εγγράφου: conference object
Περιγραφή αρχείου: 18 p.; application/pdf
Γλώσσα: English
Relation: http://link.springer.com/chapter/10.1007%2F978-3-319-23063-4_30; https://hdl.handle.net/2117/87035
DOI: 10.1007/978-3-319-23063-4_30
Διαθεσιμότητα: https://hdl.handle.net/2117/87035
https://doi.org/10.1007/978-3-319-23063-4_30
Rights: Open Access
Αριθμός Καταχώρησης: edsbas.D3EB7291
Βάση Δεδομένων: BASE
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  – Url: https://hdl.handle.net/2117/87035#
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  Data: Universitat Politècnica de Catalunya. Departament de Ciències de la Computació<br />Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals
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  Data: Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge
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  Data: The visualization of models is essential for user-friendly human-machine interactions during Process Mining. A simple graphical representation contributes to give intuitive information about the behavior of a system. However, complex systems cannot always be represented with succinct models that can be easily visualized. Quality-preserving model simplifications can be of paramount importance to alleviate the complexity of finding useful and attractive visualizations. This paper presents a collection of log-based techniques to simplify process models. The techniques trade off visual-friendly properties with quality metrics related to logs, such as fitness and precision, to avoid degrading the resulting model. The algorithms, either cast as optimization problems or heuristically guided, find simplified versions of the initial process model, and can be applied in the final stage of the process mining life-cycle, between the discovery of a process model and the deployment to the final user. A tool has been developed and tested on large logs, producing simplified process models that are one order of magnitude smaller while keeping fitness and precision under reasonable margins. ; Peer Reviewed ; Postprint (author's final draft)
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