Hypothetical answers to continuous queries over data streams

Luís Cruz-Filipe; Graça Gaspar; Isabel Nunes

doi:10.1145/3688845

Hypothetical answers to continuous queries over data streams

Luís Cruz-Filipe
, Graça Gaspar
, Isabel Nunes

Universitetsledelsen

Publikation: Konference artikel i Proceeding eller bog/rapport kapitel › Konferencebidrag i proceedings › Forskning › peer review

Abstract

Continuous queries over data streams often 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.

Originalsprog	Engelsk
Titel	AAAI 2020 - 34th AAAI Conference on Artificial Intelligence
Antal sider	8
Forlag	AAAI Press
Publikationsdato	2020
Sider	2798-2805
DOI	https://doi.org/10.1145/3688845
Status	Udgivet - 2020
Begivenhed	Conference on Artificial Intelligence - New York, USA Varighed: 7 feb. 2020 → 12 feb. 2020 Konferencens nummer: 34 https://dblp.org/db/conf/aaai/aaai2020.html

Konference

Konference	Conference on Artificial Intelligence
Nummer	34
Land/Område	USA
By	New York
Periode	07/02/2020 → 12/02/2020
Internetadresse	https://dblp.org/db/conf/aaai/aaai2020.html

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10.1145/3688845

Citationsformater

@inproceedings{d9bf1831b8e344598715e5cbeb5d702c,

title = "Hypothetical answers to continuous queries over data streams",

abstract = "Continuous queries over data streams often 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.",

keywords = "Data streams, Continuous queries, Hypothetical reasoning, Online algorithms, Epistemic semantics",

author = "Lu{\'i}s Cruz-Filipe and Gra{\c c}a Gaspar and Isabel Nunes",

note = "Publisher Copyright: Copyright {\textcopyright} 2020, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.; 34th AAAI Conference on Artificial Intelligence, AAAI 2020 ; Conference date: 07-02-2020 Through 12-02-2020; Conference on Artificial Intelligence, AAAI ; Conference date: 07-02-2020 Through 12-02-2020",

year = "2020",

doi = "10.1145/3688845",

language = "English",

pages = "2798--2805",

booktitle = "AAAI 2020 - 34th AAAI Conference on Artificial Intelligence",

publisher = "AAAI Press",

address = "United States",

url = "https://dblp.org/db/conf/aaai/aaai2020.html",

}

TY - GEN

T1 - Hypothetical answers to continuous queries over data streams

AU - Cruz-Filipe, Luís

AU - Gaspar, Graça

AU - Nunes, Isabel

N1 - Conference code: 34

PY - 2020

Y1 - 2020

N2 - Continuous queries over data streams often 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.

AB - Continuous queries over data streams often 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.

KW - Data streams

KW - Continuous queries

KW - Hypothetical reasoning

KW - Online algorithms

KW - Epistemic semantics

U2 - 10.1145/3688845

DO - 10.1145/3688845

M3 - Article in proceedings

SP - 2798

EP - 2805

BT - AAAI 2020 - 34th AAAI Conference on Artificial Intelligence

PB - AAAI Press

T2 - Conference on Artificial Intelligence

Y2 - 7 February 2020 through 12 February 2020

ER -

Hypothetical answers to continuous queries over data streams

Abstract

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