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Learning Temporal Properties from Event Logs via Sequential Analysis

Author Francesco Chiariello

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LIPIcs.TIME.2024.14.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Modal and temporal logics
  • Applied computing → Business process management
Keywords
  • Process Mining
  • Declarative Process Discovery
  • Trace Alignment
  • Sequential Analysis

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Abstract

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.

Cite As Get BibTex

Francesco Chiariello. Learning Temporal Properties from Event Logs via Sequential Analysis. In 31st International Symposium on Temporal Representation and Reasoning (TIME 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 318, pp. 14:1-14:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.TIME.2024.14

Author Details

Francesco Chiariello
  • IRIT, ANITI, University of Toulouse, France

Funding

This project has been funded by the French government as part of France 2030 (Grant agreement n°ANR-19-PI3A-0004) and by the European Union - Next Generation EU as part of the France Relance.

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