Process-level Model Repair through Instance Graph Representation: (Discussion paper)

Laura Genga, Claudia Diamantini (Corresponding author), E. Storti, Domenico Potena

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Abstract

Existing model repair techniques propose changes that are based on event-level deviations observed in a log, like inserted or skipped events, often overlooking process precision at the advantage of fitness. The present short paper aims to briefly introduce the recent proposal of an alternative approach targeting higher-level structured anomalous behaviors. To do this, the approach exploits instance graph representation of anomalous behaviors, that can be derived from the event log and the original process model. The approach demonstrates that repaired models obtained in this way show higher precision and simplicity, with only small reduction of process fitness.

Original languageEnglish
Title of host publicationSEBD 2024 : Symposium on Advanced Database Systems 2024
Subtitle of host publicationProceedings of the 32nd Symposium on Advanced Database Systems
EditorsMaurizio Atzori, Paolo Ciaccia, Michelangelo Ceci, Federica Mandreoli, Donato Malerba, Manuela Sanguinetti, Antonio Pellicani, Federico Motta
PublisherCEUR-WS.org
Pages359-367
Number of pages9
Publication statusPublished - 2024
Event32nd Italian Symposium on Advanced Database Systems, SEBD 2024 - Villasimius, Italy
Duration: 23 Jun 202426 Jun 2024

Publication series

NameCEUR Workshop Proceedings
Volume3741
ISSN (Electronic)1613-0073

Conference

Conference32nd Italian Symposium on Advanced Database Systems, SEBD 2024
Country/TerritoryItaly
CityVillasimius
Period23/06/2426/06/24

Keywords

  • Model Repair
  • Process Mining
  • Subgraph Mining

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