Dealing with temporal indeterminacy in relational databases: An AI methodology

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Anselma, Luca
Piovesan, Luca
Terenziani, Paolo
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2019
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Abstract

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.

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AI Communications

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32

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3

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Subject

Artificial intelligence

Cognitive and computational psychology

Science & Technology

Technology

Computer Science, Artificial Intelligence

Computer Science

Temporal data

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Anselma, L; Piovesan, L; Terenziani, P, Dealing with temporal indeterminacy in relational databases: An AI methodology, AI Communications, 2019, 32 (3), pp. 207-221

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