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Mining Significant Temporal Networks Is Polynomial

Authors Guido Sciavicco , Matteo Zavatteri , Tiziano Villa

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LIPIcs.TIME.2020.11.pdf
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Subject Classification

ACM Subject Classification
  • Computing methodologies → Temporal reasoning
  • Information systems → Data mining
  • Computing methodologies → Planning and scheduling
  • Mathematics of computing → Graph algorithms
Keywords
  • Mining temporal constraints
  • cstnud
  • uncertainty
  • significant temporal network

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Abstract

A Conditional Simple Temporal Network with Uncertainty and Decisions (CSTNUD) is a formalism that tackles controllable and uncontrollable durations as well as controllable and uncontrollable choices simultaneously. In the classic top-down model-based engineering approach, a designer builds a CSTNUD to model, validate and execute some temporal plan of interest. Instead, in this paper, we investigate the bottom-up approach by providing a deterministic polynomial time algorithm to mine a CSTNUD from a set of execution traces (i.e., a log). This paper paves the way for the design of controllable temporal networks mined from traces that also contain information on uncontrollable events.

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Guido Sciavicco, Matteo Zavatteri, and Tiziano Villa. Mining Significant Temporal Networks Is Polynomial. In 27th International Symposium on Temporal Representation and Reasoning (TIME 2020). Leibniz International Proceedings in Informatics (LIPIcs), Volume 178, pp. 11:1-11:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2020) https://doi.org/10.4230/LIPIcs.TIME.2020.11

Author Details

Guido Sciavicco
  • Department of Mathematics and Computer Science, University of Ferrara, Italy
Matteo Zavatteri
  • Department of Computer Science, University of Verona, Italy
Tiziano Villa
  • Department of Computer Science, University of Verona, Italy

Funding

This work was partially supported by MIUR, Project Italian Outstanding Departments, 2018-2022 and by INdAM, GNCS 2020, Project Strategic Reasoning and Automated Synthesis of Multi-Agent Systems.

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