TY - GEN
T1 - Discovering Guard Stage Milestone Models Through Hierarchical Clustering
AU - M’Baba, Leyla Moctar
AU - Sellami, Mohamed
AU - Assy, Nour
AU - Gaaloul, Walid
AU - Nanne, Mohamedade Farouk
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024/1/1
Y1 - 2024/1/1
N2 - Processes executed on enterprise Information Systems (IS), such as ERP and CMS, are artifact-centric. The execution of these processes is driven by the creation and evolution of business entities called artifacts. Several artifact-centric modeling languages were proposed to capture the specificity of these processes. One of the most used artifact-centric modeling languages is the Guard Stage Milestone (GSM) language. It represents an artifact-centric process as an information model and a lifecycle. The lifecycle groups activities in stages with data conditions as guards. The hierarchy between the stages is based on common conditions. However, existing works do not discover this hierarchy nor the data conditions, as they considered them to be already available. They also do not discover GSM models directly from event logs. They discover Petri nets and translate them into GSM models. To fill this gap, we propose in this paper a discovery approach based on hierarchical clustering. We use invariants detection to discover data conditions and information gain of common conditions to cluster stages. The approach does not rely on domain knowledge nor translation mechanisms. It was implemented and evaluated using a blockchain case study.
AB - Processes executed on enterprise Information Systems (IS), such as ERP and CMS, are artifact-centric. The execution of these processes is driven by the creation and evolution of business entities called artifacts. Several artifact-centric modeling languages were proposed to capture the specificity of these processes. One of the most used artifact-centric modeling languages is the Guard Stage Milestone (GSM) language. It represents an artifact-centric process as an information model and a lifecycle. The lifecycle groups activities in stages with data conditions as guards. The hierarchy between the stages is based on common conditions. However, existing works do not discover this hierarchy nor the data conditions, as they considered them to be already available. They also do not discover GSM models directly from event logs. They discover Petri nets and translate them into GSM models. To fill this gap, we propose in this paper a discovery approach based on hierarchical clustering. We use invariants detection to discover data conditions and information gain of common conditions to cluster stages. The approach does not rely on domain knowledge nor translation mechanisms. It was implemented and evaluated using a blockchain case study.
KW - Artifact-Centric Event Logs
KW - Artifact-Centric Processes
KW - Guard-Stage-Milestone
KW - Process mining
U2 - 10.1007/978-3-031-46846-9_13
DO - 10.1007/978-3-031-46846-9_13
M3 - Conference contribution
AN - SCOPUS:85175985948
SN - 9783031468452
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 239
EP - 256
BT - Cooperative Information Systems - 29th International Conference, CoopIS 2023, Proceedings
A2 - Sellami, Mohamed
A2 - Gaaloul, Walid
A2 - Vidal, Maria-Esther
A2 - van Dongen, Boudewijn
A2 - Panetto, Hervé
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Cooperative Information Systems, CoopIS 2023
Y2 - 30 October 2023 through 3 November 2023
ER -