Dealing with temporal indeterminacy in relational databases: An AI methodology

Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including AI and relational Temporal Databases (TDB). While temporally imprecise information has been widely studied by the AI community, only few approaches have faced temporal inde...

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Bibliographic Details
Published in:Ai communications 2019-01, Vol.32 (3), p.207-221
Main Authors: Anselma, Luca, Piovesan, Luca, Terenziani, Paolo
Format: Article
Language:English
Subjects:
Artificial intelligence
Relational data bases
Representations
Semantics
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Summary:Time is pervasive of the human way of approaching reality, so that it has been widely studied in many research areas, including AI and relational Temporal Databases (TDB). While temporally imprecise information has been widely studied by the AI community, only few approaches have faced temporal indeterminacy (in particular, “don’t know exactly when” indeterminacy) in TDBs. Indeed, as we will show in this paper, the treatment of time in general, and of temporal indeterminacy in particular, involves the introduction of implicit forms of data representation in TDBs. As a consequence, we propose a new AI-style methodology to cope with temporal indeterminacy in TDBs. Specifically, we show that typical AI notions and techniques, such as making explicit the semantics of the representation formalism, and adopting symbolic manipulation techniques based on such a semantics, can be fruitfully exploited in the development of a “principled” treatment of indeterminate time in relational databases.
ISSN:0921-7126
1875-8452
DOI:10.3233/AIC-190619