The RALph miner for automated discovery and verification of resource-aware process models

Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emp...

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Veröffentlicht in:Software and systems modeling Jg. 19; H. 6; S. 1415 - 1441
Hauptverfasser: Cabanillas, Cristina, Ackermann, Lars, Schönig, Stefan, Sturm, Christian, Mendling, Jan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2020
Springer Nature B.V
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ISSN:1619-1366, 1619-1374
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Abstract Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.
AbstractList Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities performed, their timestamps and the resources that were involved in their execution. Recent approaches to process discovery put a special emphasis on (human) resources, aiming at constructing resource-aware process models that contain the inferred resource assignment constraints. Such constraints can be complex and process discovery approaches so far have missed the opportunity to represent expressive resource assignments graphically together with process models. A subsequent verification of the extracted resource-aware process models is required in order to check the proper utilisation of resources according to the resource assignments. So far, research on discovering resource-aware process models has assumed that models can be put into operation without modification and checking. Integrating resource mining and resource-aware process model verification faces the challenge that different types of resource assignment languages are used for each task. In this paper, we present an integrated solution that comprises (i) a resource mining technique that builds upon a highly expressive graphical notation for defining resource assignments; and (ii) automated model-checking support to validate the discovered resource-aware process models. All the concepts reported in this paper have been implemented and evaluated in terms of feasibility and performance.
Author Sturm, Christian
Schönig, Stefan
Mendling, Jan
Cabanillas, Cristina
Ackermann, Lars
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  surname: Cabanillas
  fullname: Cabanillas, Cristina
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  organization: University of Seville, Vienna University of Economics and Business
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  givenname: Lars
  surname: Ackermann
  fullname: Ackermann, Lars
  organization: University of Bayreuth
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  surname: Schönig
  fullname: Schönig, Stefan
  organization: University of Regensburg
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  givenname: Christian
  surname: Sturm
  fullname: Sturm, Christian
  organization: University of Bayreuth
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  givenname: Jan
  surname: Mendling
  fullname: Mendling, Jan
  organization: Vienna University of Economics and Business
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crossref_primary_10_1016_j_artmed_2022_102434
crossref_primary_10_1007_s12599_024_00894_3
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Issue 6
Keywords Process mining
Resource assignment
Organisational mining
Process verification
RALph
Resource mining
Model checking
Language English
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  doi: 10.1007/11431855_16
– ident: 820_CR55
– ident: 820_CR61
– ident: 820_CR8
– volume: 2
  start-page: 208
  year: 2009
  ident: 820_CR34
  publication-title: Trans. Petri Nets Other Models Concurr.
  doi: 10.1007/978-3-642-00899-3\_12
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Snippet Automated process discovery is a technique that extracts models of executed processes from event logs. Logs typically include information about the activities...
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SubjectTerms Automation
Compilers
Computer Science
Information Systems Applications (incl.Internet)
Interpreters
IT in Business
Programming Languages
Programming Techniques
Software Engineering
Software Engineering/Programming and Operating Systems
Special Section Paper
Verification
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Title The RALph miner for automated discovery and verification of resource-aware process models
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