DROPS

Document

DOI: 10.4230/LIPIcs.TIME.2024.14

Learning Temporal Properties from Event Logs via Sequential Analysis

Authors: Francesco Chiariello

Published in: LIPIcs, Volume 318, 31st International Symposium on Temporal Representation and Reasoning (TIME 2024)

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

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)

Copy BibTex To Clipboard

@InProceedings{chiariello:LIPIcs.TIME.2024.14,
  author =	{Chiariello, Francesco},
  title =	{{Learning Temporal Properties from Event Logs via Sequential Analysis}},
  booktitle =	{31st International Symposium on Temporal Representation and Reasoning (TIME 2024)},
  pages =	{14:1--14:14},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-349-2},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{318},
  editor =	{Sala, Pietro and Sioutis, Michael and Wang, Fusheng},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.TIME.2024.14},
  URN =		{urn:nbn:de:0030-drops-212217},
  doi =		{10.4230/LIPIcs.TIME.2024.14},
  annote =	{Keywords: Process Mining, Declarative Process Discovery, Trace Alignment, Sequential Analysis}
}

Search Results

Documents authored by Chiariello, Francesco

Learning Temporal Properties from Event Logs via Sequential Analysis

Abstract

Cite as

Thanks for your feedback!

Could not send message