An AI approach to temporal indeterminacy in relational databases

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including Artificial Intelligence (AI) and relational Temporal Databases (TDB). Indeed, while thousands of TDB papers have been devoted to the treatment of determinate time, only few approaches have faced temporal indeterminacy (i.e., “don’t know exactly when” indeterminacy). In this paper, we propose a new AI-based methodology to approach temporal indeterminacy in relational DBs. We show that typical AI techniques, such as studying the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, are very important in the treatment of indeterminate time in relational databases.

Original languageEnglish
Title of host publicationAdvances in Artificial Intelligence – IBERAMIA 2018 - 16th Ibero-American Conference on AI, Proceedings
EditorsEduardo Fermé, Guillermo R. Simari, Flabio Gutiérrez Segura, José Antonio Rodríguez Melquiades
PublisherSpringer Verlag
Pages16-28
Number of pages13
ISBN (Print)9783030039271
DOIs
Publication statusPublished - 2018
Event16th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2018 - Trujillo, Peru
Duration: 13 Nov 201816 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11238 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th Ibero-American Conference on Artificial Intelligence, IBERAMIA 2018
Country/TerritoryPeru
CityTrujillo
Period13/11/1816/11/18

Keywords

  • Data representation and semantics
  • Query semantics
  • Symbolic manipulation
  • Temporal data

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