@inproceedings{f5e7fbcfe70b491682e4b17162b4e853,
title = "Process-level Model Repair through Instance Graph Representation: (Discussion paper)",
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.",
keywords = "Model Repair, Process Mining, Subgraph Mining",
author = "Laura Genga and Claudia Diamantini and E. Storti and Domenico Potena",
year = "2024",
language = "English",
series = "CEUR Workshop Proceedings",
publisher = "CEUR-WS.org",
pages = "359--367",
editor = "Maurizio Atzori and Paolo Ciaccia and Michelangelo Ceci and Federica Mandreoli and Donato Malerba and Manuela Sanguinetti and Antonio Pellicani and Federico Motta",
booktitle = "SEBD 2024 : Symposium on Advanced Database Systems 2024",
note = "32nd Italian Symposium on Advanced Database Systems, SEBD 2024 ; Conference date: 23-06-2024 Through 26-06-2024",
}