research-article

Probabilistic Maximum Range-Sum Queries on Spatial Database

Authors:

Lei ChenAuthors Info & Claims

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

Pages 159 - 168

https://doi.org/10.1145/3347146.3359376

Published: 05 November 2019 Publication History

Abstract

Maximum Range-Sum (MaxRS) query is an important operator in spatial database for retrieving regions of interest (ROIs). Given a rectangular query size a × b and a set of spatial objects associated with positive weights, MaxRS retrieves rectangular regions Q of size a × b, such that the sum of object weights covered by Q (i.e., range-sum) is maximized. Due to the inaccuracy of the location acquisition, the collected locations of spatial objects are inherently uncertain and imprecise, which can be modeled by uncertain objects. In this paper, we propose a Probabilistic Maximum Range-Sum (PMaxRS) query over uncertain spatial objects, which obtains a set γ* of rectangles such that the probability that each region Q ϵ γ* has the maximum range-sum exceeds a user-specified threshold Pt. We show that determining whether a given region Q is #P-complete. To tackle the hardness, we introduce the PMaxRS_Framework based on pruning and refinement strategies. In the pruning step, we propose a candidate generation technique to reduce the search space. In the refinement step, we design an efficient sampling-based approximation algorithm to verify the remaining candidate regions. Extensive experiments are conducted to demonstrate the effectiveness and efficiency of our algorithms.

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Cited By

Chen JGao HZhang KWang JLuo YWu ZLi J(2024)On Efficiently Processing MIT Queries in Trajectory DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336194836:7(3329-3347)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3361948
Chen JGao HZhang KWang JLuo YWu ZLi J(2023)Towards Efficient MIT query in Trajectory Data2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00170(2194-2206)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00170
Li HLu HJensen CTang BCheema M(2022)Spatial Data Quality in the Internet of Things: Management, Exploitation, and ProspectsACM Computing Surveys10.1145/349833855:3(1-41)Online publication date: 3-Feb-2022
https://dl.acm.org/doi/10.1145/3498338
Show More Cited By

Index Terms

Probabilistic Maximum Range-Sum Queries on Spatial Database
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
2. Mathematics of computing
  1. Probability and statistics
    1. Probabilistic algorithms

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cover image ACM Conferences

SIGSPATIAL '19: Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 2019

648 pages

ISBN:9781450369091

DOI:10.1145/3347146

Copyright © 2019 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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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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Association for Computing Machinery

New York, NY, United States

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Published: 05 November 2019

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SIGSPATIAL '19

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SIGSPATIAL

SIGSPATIAL '19: 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 5 - 8, 2019

IL, Chicago, USA

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SIGSPATIAL '19 Paper Acceptance Rate 34 of 161 submissions, 21%;

Overall Acceptance Rate 257 of 1,238 submissions, 21%

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Cited By

Chen JGao HZhang KWang JLuo YWu ZLi J(2024)On Efficiently Processing MIT Queries in Trajectory DataIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.336194836:7(3329-3347)Online publication date: Jul-2024
https://doi.org/10.1109/TKDE.2024.3361948
Chen JGao HZhang KWang JLuo YWu ZLi J(2023)Towards Efficient MIT query in Trajectory Data2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00170(2194-2206)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00170
Li HLu HJensen CTang BCheema M(2022)Spatial Data Quality in the Internet of Things: Management, Exploitation, and ProspectsACM Computing Surveys10.1145/349833855:3(1-41)Online publication date: 3-Feb-2022
https://dl.acm.org/doi/10.1145/3498338
Zhang KGao HHan XChen JLi J(2022)Maximizing Range Sum in Trajectory Data2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00061(755-766)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00061
Chen JZhang KRen TWu ZGao H(2022)GAM: A GPU-Accelerated Algorithm for MaxRS Queries in Road NetworksJournal of Computer Science and Technology10.1007/s11390-022-2330-337:5(1005-1025)Online publication date: 30-Sep-2022
https://doi.org/10.1007/s11390-022-2330-3
Tahmasbi ATrajcevski G(2022)Maximum Range-Sum for Dynamically Occurring Objects with Decaying WeightsAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_18(238-252)Online publication date: 29-Aug-2022
https://doi.org/10.1007/978-3-031-15740-0_18
Mas-ud Hussain MMostafiz MMahmud STrajcevski GEunus Ali M(2020)Conditional MaxRS Query for Evolving Spatial DataFrontiers in Big Data10.3389/fdata.2020.000203Online publication date: 19-Jun-2020
https://doi.org/10.3389/fdata.2020.00020
Züfle A(2020)Uncertain Spatial Data Management: An OverviewHandbook of Big Geospatial Data10.1007/978-3-030-55462-0_14(355-397)Online publication date: 17-Dec-2020
https://doi.org/10.1007/978-3-030-55462-0_14

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