research-article

Towards indexing functions: answering scalar product queries

Authors:

Bojana Dimcheva,

Donald KossmannAuthors Info & Claims

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

Pages 241 - 252

https://doi.org/10.1145/2588555.2610493

Published: 18 June 2014 Publication History

Abstract

We consider a broad category of analytic queries, denoted by scalar product queries, which can be expressed as a scalar product between a known function over multiple database attributes and an unknown set of parameters. More specifically, given a set of d-dimensional data points, we retrieve all points x which satisfy an inequality given by a scalar product: <a, f(x)> <= b. We assume that the function f() is application specific and known apriori, while the query parameters a and the inequality parameter b are known only at the time of querying.

Efficiently answering such scalar product queries are essential in a wide range of applications including evaluation of complex SQL functions, time series prediction, scientific simulation, and active learning. Although some specific subclasses of the aforementioned scalar product queries and their applications have been studied in computational geometry, machine learning, and in moving object queries, surprisingly no generalized indexing scheme has been proposed for efficiently computing scalar product queries.

We present a lightweight, yet scalable, dynamic, and generalized indexing scheme, called the planar index, for answering scalar product queries in an accurate manner, which is based on the idea of indexing function f(x) for each data point x using multiple sets of parallel hyperplanes. Planar index has loglinear indexing time and linear space complexity, and the query time ranges from logarithmic to being linear in the number of data points. Based on an extensive set of experiments on several real-world and synthetic datasets, we show that planar index is not only scalable and dynamic, but also effective in various real-world applications including intersection finding between moving objects and active learning.

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Cited By

Nosrati MCai Y(2022)Verifying the Correctness of Analytic Query ResultsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303731334:9(4527-4537)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TKDE.2020.3037313
Lin WWang KZhang ZFu AWong RLong CMiao C(2022)Towards Secure and Efficient Equality Conjunction Search Over Outsourced DatabasesIEEE Transactions on Cloud Computing10.1109/TCC.2020.297517510:2(1445-1461)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TCC.2020.2975175
Yang GCai Y(2018)Querying a Collection of Continuous FunctionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.280293630:9(1783-1795)Online publication date: 1-Sep-2018
https://doi.org/10.1109/TKDE.2018.2802936
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Index Terms

Towards indexing functions: answering scalar product queries
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

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cover image ACM Conferences

SIGMOD '14: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data

June 2014

1645 pages

ISBN:9781450323765

DOI:10.1145/2588555

General Chairs:
Curtis Dyreson
Utah State University, USA
,
Feifei Li
University of Utah, USA
,
Program Chair:
M. Tamer Özsu
University of Waterloo, Canada

Copyright © 2014 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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Published: 18 June 2014

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SIGMOD/PODS'14: International Conference on Management of Data

June 22 - 27, 2014

Utah, Snowbird, USA

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SIGMOD '14 Paper Acceptance Rate 107 of 421 submissions, 25%;

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

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Cited By

Nosrati MCai Y(2022)Verifying the Correctness of Analytic Query ResultsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.303731334:9(4527-4537)Online publication date: 1-Sep-2022
https://doi.org/10.1109/TKDE.2020.3037313
Lin WWang KZhang ZFu AWong RLong CMiao C(2022)Towards Secure and Efficient Equality Conjunction Search Over Outsourced DatabasesIEEE Transactions on Cloud Computing10.1109/TCC.2020.297517510:2(1445-1461)Online publication date: 1-Apr-2022
https://doi.org/10.1109/TCC.2020.2975175
Yang GCai Y(2018)Querying a Collection of Continuous FunctionsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2018.280293630:9(1783-1795)Online publication date: 1-Sep-2018
https://doi.org/10.1109/TKDE.2018.2802936
Crucianu M(2018)Image Retrieval and Relevance FeedbackEncyclopedia of Database Systems10.1007/978-1-4614-8265-9_1016(1790-1795)Online publication date: 7-Dec-2018
https://doi.org/10.1007/978-1-4614-8265-9_1016
Yang GCai YHu Z(2016)Authentication of function queries2016 IEEE 32nd International Conference on Data Engineering (ICDE)10.1109/ICDE.2016.7498252(337-348)Online publication date: May-2016
https://doi.org/10.1109/ICDE.2016.7498252
Stoian AFerecatu MBenois-Pineau JCrucianu M(2015)Scalable action localization with kernel-space hashing2015 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP.2015.7350799(257-261)Online publication date: Sep-2015
https://doi.org/10.1109/ICIP.2015.7350799

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