research-article

Threshold query optimization for uncertain data

Authors:

Sarvjeet Singh,

Sunil PrabhakarAuthors Info & Claims

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

Pages 315 - 326

https://doi.org/10.1145/1807167.1807203

Published: 06 June 2010 Publication History

Abstract

The probabilistic threshold query (PTQ) is one of the most common queries in uncertain databases, where all results satisfying the query with probabilities that meet the threshold requirement are returned. PTQ is used widely in nearest-neighbor queries, range queries, ranking queries, etc. In this paper, we investigate the general PTQ for arbitrary SQL queries that involve selections, projections and joins. The uncertain database model that we use is one that combines both attribute and tuple uncertainty as well as correlations between arbitrary attribute sets. We address the PTQ optimization problem that aims at improving the efficiency of PTQ query execution by enabling alternative query plan enumeration for optimization. We propose general optimization rules as well as rules specifically for selections, projections and joins. We introduce a threshold operator (τ-operator) to the query plan and show it is generally desirable to push down the τ-operator as much as possible.

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Cited By

Gabory ELiu CLoukides GPissis SZuba W(2024)Space-Efficient Indexes for Uncertain Strings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00367(4828-4842)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00367
Suciu DOlteanu DRé CKoch C(2022)Probabilistic Databases10.1007/978-3-031-01879-4Online publication date: 2-Mar-2022
https://doi.org/10.1007/978-3-031-01879-4
Li LWang HLi JGao H(2020)A survey of uncertain data managementFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-7063-z14:1(162-190)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s11704-017-7063-z
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Index Terms

Threshold query optimization for uncertain data
1. Information systems
  1. Information systems applications

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cover image ACM Conferences

SIGMOD '10: Proceedings of the 2010 ACM SIGMOD International Conference on Management of data

June 2010

1286 pages

ISBN:9781450300322

DOI:10.1145/1807167

General Chair:
Ahmed Elmagarmid
Purdue University, USA
,
Program Chair:
Divyakant Agrawal
University of California at Santa Barbara, USA

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOD: ACM Special Interest Group on Management of Data

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New York, NY, United States

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Published: 06 June 2010

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SIGMOD/PODS '10

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SIGMOD

SIGMOD/PODS '10: International Conference on Management of Data

June 6 - 10, 2010

Indiana, Indianapolis, USA

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Overall Acceptance Rate 785 of 4,003 submissions, 20%

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Cited By

Gabory ELiu CLoukides GPissis SZuba W(2024)Space-Efficient Indexes for Uncertain Strings2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00367(4828-4842)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00367
Suciu DOlteanu DRé CKoch C(2022)Probabilistic Databases10.1007/978-3-031-01879-4Online publication date: 2-Mar-2022
https://doi.org/10.1007/978-3-031-01879-4
Li LWang HLi JGao H(2020)A survey of uncertain data managementFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-017-7063-z14:1(162-190)Online publication date: 1-Feb-2020
https://dl.acm.org/doi/10.1007/s11704-017-7063-z
Zhang YWang HYang LLi J(2018)Efficient histogram-based range query estimation for dirty dataFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-016-5551-112:5(984-999)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.1007/s11704-016-5551-1
Wang ZYao BCheng RGao XZou LGuan HGuo M(2016)SMeGeoinformatica10.1007/s10707-015-0230-120:1(19-58)Online publication date: 1-Jan-2016
https://dl.acm.org/doi/10.1007/s10707-015-0230-1
Li JLiu CZhou RYu J(2014)Quasi-SLCA Based Keyword QueryProcessing over Probabilistic XML DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2013.6726:4(957-969)Online publication date: 1-Apr-2014
https://dl.acm.org/doi/10.1109/TKDE.2013.67
Gao FJain RPrabhakar SSi L(2014)ProbKS: Keyword Search on Probabilistic Spatial DataDatabase Systems for Advanced Applications10.1007/978-3-662-43984-5_30(388-402)Online publication date: 11-Jul-2014
https://doi.org/10.1007/978-3-662-43984-5_30
Li JWang H(2014)Range Queries on Uncertain DataAlgorithms and Computation10.1007/978-3-319-13075-0_26(326-337)Online publication date: 8-Nov-2014
https://doi.org/10.1007/978-3-319-13075-0_26
Dylla MTheobald MMiliaraki I(2013)Top-k query processing in probabilistic databases with non-materialized viewsProceedings of the 2013 IEEE International Conference on Data Engineering (ICDE 2013)10.1109/ICDE.2013.6544819(122-133)Online publication date: 8-Apr-2013
https://dl.acm.org/doi/10.1109/ICDE.2013.6544819
Wang YLi XLi XWang Y(2013)A survey of queries over uncertain dataKnowledge and Information Systems10.1007/s10115-013-0638-637:3(485-530)Online publication date: 30-Apr-2013
https://doi.org/10.1007/s10115-013-0638-6
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