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Autori principali: Cruz-Filipe, Luís, Gaspar, Graça, Nunes, Isabel
Natura: Preprint
Pubblicazione: 2019
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Accesso online:https://arxiv.org/abs/1905.09610
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author Cruz-Filipe, Luís
Gaspar, Graça
Nunes, Isabel
author_facet Cruz-Filipe, Luís
Gaspar, Graça
Nunes, Isabel
contents Continuous queries over data streams may suffer from blocking operations and/or unbound wait, which may delay answers until some relevant input arrives through the data stream. These delays may turn answers, when they arrive, obsolete to users who sometimes have to make decisions with no help whatsoever. Therefore, it can be useful to provide hypothetical answers - "given the current information, it is possible that X will become true at time t" - instead of no information at all. In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information.
format Preprint
id arxiv_https___arxiv_org_abs_1905_09610
institution arXiv
publishDate 2019
record_format arxiv
spellingShingle Hypothetical answers to continuous queries over data streams
Cruz-Filipe, Luís
Gaspar, Graça
Nunes, Isabel
Programming Languages
Artificial Intelligence
Continuous queries over data streams may suffer from blocking operations and/or unbound wait, which may delay answers until some relevant input arrives through the data stream. These delays may turn answers, when they arrive, obsolete to users who sometimes have to make decisions with no help whatsoever. Therefore, it can be useful to provide hypothetical answers - "given the current information, it is possible that X will become true at time t" - instead of no information at all. In this paper we present a semantics for queries and corresponding answers that covers such hypothetical answers, together with an online algorithm for updating the set of facts that are consistent with the currently available information.
title Hypothetical answers to continuous queries over data streams
topic Programming Languages
Artificial Intelligence
url https://arxiv.org/abs/1905.09610