License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.TIME.2023.4
URN: urn:nbn:de:0030-drops-190945
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/19094/
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Amico, Beatrice ; Combi, Carlo ; Rizzi, Romeo ; Sala, Pietro

Discovering Predictive Dependencies on Multi-Temporal Relations

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LIPIcs-TIME-2023-4.pdf (0.9 MB)

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.

BibTeX - Entry

@InProceedings{amico_et_al:LIPIcs.TIME.2023.4,
  author =	{Amico, Beatrice and Combi, Carlo and Rizzi, Romeo and Sala, Pietro},
  title =	{{Discovering Predictive Dependencies on Multi-Temporal Relations}},
  booktitle =	{30th International Symposium on Temporal Representation and Reasoning (TIME 2023)},
  pages =	{4:1--4:19},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-298-3},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{278},
  editor =	{Artikis, Alexander and Bruse, Florian and Hunsberger, Luke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/19094},
  URN =		{urn:nbn:de:0030-drops-190945},
  doi =		{10.4230/LIPIcs.TIME.2023.4},
  annote =	{Keywords: temporal databases, temporal data mining, functional dependencies}
}

Keywords: temporal databases, temporal data mining, functional dependencies
Collection: 30th International Symposium on Temporal Representation and Reasoning (TIME 2023)
Issue Date: 2023
Date of publication: 18.09.2023

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