2026-07-23T22:35:39Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:21221 2024-10-22T05:39:58Z ddc:004 open_access

Learning Temporal Properties from Event Logs via Sequential Analysis Chiariello, Francesco Process Mining Declarative Process Discovery Trace Alignment Sequential Analysis In this work, we present a novel approach to learning Linear Temporal Logic (LTL) formulae from event logs by leveraging statistical techniques from sequential analysis. In particular, we employ the Sequential Probability Ratio Test (SPRT), using Trace Alignment to quantify the discrepancy between a trace and a candidate LTL formula. We then test the proposed approach in a controlled experimental setting and highlight its advantages, which include robustness to noise and data efficiency. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Francesco Chiariello 2024 Is Part Of LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.TIME.2024.14 urn:nbn:de:0030-drops-212217 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.14 eng https://creativecommons.org/licenses/by/4.0/legalcode