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Discovering Predictive Dependencies on Multi-Temporal Relations

Authors Beatrice Amico , Carlo Combi , Romeo Rizzi , Pietro Sala

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Beatrice Amico
  • Department of Computer Science, University of Verona, Italy
Carlo Combi
  • Department of Computer Science, University of Verona, Italy
Romeo Rizzi
  • Department of Computer Science, University of Verona, Italy
Pietro Sala
  • Department of Computer Science, University of Verona, Italy

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Beatrice Amico, Carlo Combi, Romeo Rizzi, and Pietro Sala. Discovering Predictive Dependencies on Multi-Temporal Relations. In 30th International Symposium on Temporal Representation and Reasoning (TIME 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 278, pp. 4:1-4:19, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.TIME.2023.4

Abstract

In this paper, we propose a methodology for deriving a new kind of approximate temporal functional dependencies, called Approximate Predictive Functional Dependencies (APFDs), based on a three-window framework and on a multi-temporal relational model. Different features are proposed for the Observation Window (OW), where we observe predictive data, for the Waiting Window (WW), and for the Prediction Window (PW), where the predicted event occurs. We then discuss the concept of approximation for such APFDs, introduce two new error measures. We prove that the problem of deriving APFDs is intractable. Moreover, we discuss some preliminary results in deriving APFDs from real clinical data using MIMIC III dataset, related to patients from Intensive Care Units.

Subject Classification

ACM Subject Classification
  • Information systems → Relational database model
  • Information systems → Data mining
Keywords
  • temporal databases
  • temporal data mining
  • functional dependencies

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