Hypothetical answers to continuous queries over data streams
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 whatso...
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creator | Cruz-Filipe, Luís Gaspar, Graça Nunes, Isabel |
description | 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. |
doi_str_mv | 10.48550/arxiv.1905.09610 |
format | Article |
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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.</description><identifier>DOI: 10.48550/arxiv.1905.09610</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Programming Languages</subject><creationdate>2019-05</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/1905.09610$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.1905.09610$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Cruz-Filipe, Luís</creatorcontrib><creatorcontrib>Gaspar, Graça</creatorcontrib><creatorcontrib>Nunes, Isabel</creatorcontrib><title>Hypothetical answers to continuous queries over data streams</title><description>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.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Programming Languages</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotj7FOwzAUAL0woMIHMOEfSHgvdmxHYkEVUKRKLN2j5-RZRGrjYjuF_j2iMN12uhPiDqHWrm3hgdL3dKqxg7aGziBci8fN-RjLB5dpoL2kOX9xyrJEOcS5TPMSlyw_F04TZxlPnORIhWQuiemQb8RVoH3m23-uxO7lebfeVNv317f107YiY6EaA6B2ZBodLHplPQeDaLEF5U1wo2PHQA4HFZzVjdUdNsY3Y-cVYtCgVuL-T3vJ749pOlA6978b_WVD_QA9aUJa</recordid><startdate>20190523</startdate><enddate>20190523</enddate><creator>Cruz-Filipe, Luís</creator><creator>Gaspar, Graça</creator><creator>Nunes, Isabel</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20190523</creationdate><title>Hypothetical answers to continuous queries over data streams</title><author>Cruz-Filipe, Luís ; Gaspar, Graça ; Nunes, Isabel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a670-df0148a624f71b37bef61171503b6f8d8e8e0a81c3f8742749126b2d9b311f403</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Programming Languages</topic><toplevel>online_resources</toplevel><creatorcontrib>Cruz-Filipe, Luís</creatorcontrib><creatorcontrib>Gaspar, Graça</creatorcontrib><creatorcontrib>Nunes, Isabel</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Cruz-Filipe, Luís</au><au>Gaspar, Graça</au><au>Nunes, Isabel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Hypothetical answers to continuous queries over data streams</atitle><date>2019-05-23</date><risdate>2019</risdate><abstract>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.</abstract><doi>10.48550/arxiv.1905.09610</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Artificial Intelligence Computer Science - Programming Languages |
title | Hypothetical answers to continuous queries over data streams |
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