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Indexing the positions of continuously moving objects

Authors:

Simonas Šaltenis,

Christian S. Jensen,

Scott T. Leutenegger, and

Mario A. LopezAuthors Info & Claims

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

May 2000

Pages 331 - 342

https://doi.org/10.1145/342009.335427

Published: 16 May 2000 Publication History

Abstract

The coming years will witness dramatic advances in wireless communications as well as positioning technologies. As a result, tracking the changing positions of objects capable of continuous movement is becoming increasingly feasible and necessary. The present paper proposes a novel, R*-tree based indexing technique that supports the efficient querying of the current and projected future positions of such moving objects. The technique is capable of indexing objects moving in one-, two-, and three-dimensional space. Update algorithms enable the index to accommodate a dynamic data set, where objects may appear and disappear, and where changes occur in the anticipated positions of existing objects. A comprehensive performance study is reported.

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Cited By

Li XHidayat ATaniar DCheema M(2024)Continuous Monitoring of Reverse Approximate Nearest Neighbour Queries on Road NetworkInformation Sciences10.1016/j.ins.2024.120464(120464)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120464
Qian STian Z(2024)A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarityEvolutionary Intelligence10.1007/s12065-024-00926-717:4(3031-3041)Online publication date: 25-Mar-2024
https://doi.org/10.1007/s12065-024-00926-7
Raj TVallathan GPerumal ESudhakar PA. J(2024)Future and Research Perspectives of Spatiotemporal Data Management MethodsSpatiotemporal Data Analytics and Modeling10.1007/978-981-99-9651-3_12(235-245)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-981-99-9651-3_12
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Index Terms

Indexing the positions of continuously moving objects
1. Information systems
  1. Information retrieval
    1. Document representation
  2. Information systems applications

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cover image ACM Conferences

SIGMOD '00: Proceedings of the 2000 ACM SIGMOD international conference on Management of data

May 2000

604 pages

ISBN:1581132174

DOI:10.1145/342009

Chairmen:
Maggie Dunham
Southern Methodist Univ.
,
Jeffrey F. Naughton
Univ. of Wisconsin-Madison
,
Weidong Chen
Southern Methodist Univ.
,
Nick Koudas
AT &T Labs

ACM SIGMOD Record Volume 29, Issue 2
June 2000
609 pages
ISSN:0163-5808
DOI:10.1145/335191
Editors:
Weidong Chen
Southern Methodist Univ., Dallas, TX
,
Jeffrey Naughton
Univ. of Wisconsin-Madison, Madison
,
Philip A. Bernstein
Microsoft
Issue’s Table of Contents

Copyright © 2000 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 May 2000

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SIGMOD/PODS00: ACM International Conference on Management of Data and Symposium on Principles of Database Systems

May 15 - 18, 2000

Texas, Dallas, USA

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SIGMOD '00 Paper Acceptance Rate 42 of 248 submissions, 17%;

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

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Cited By

Li XHidayat ATaniar DCheema M(2024)Continuous Monitoring of Reverse Approximate Nearest Neighbour Queries on Road NetworkInformation Sciences10.1016/j.ins.2024.120464(120464)Online publication date: Mar-2024
https://doi.org/10.1016/j.ins.2024.120464
Qian STian Z(2024)A nearest neighbor query method for searching objects with time and location informations based on spatiotemporal similarityEvolutionary Intelligence10.1007/s12065-024-00926-717:4(3031-3041)Online publication date: 25-Mar-2024
https://doi.org/10.1007/s12065-024-00926-7
Raj TVallathan GPerumal ESudhakar PA. J(2024)Future and Research Perspectives of Spatiotemporal Data Management MethodsSpatiotemporal Data Analytics and Modeling10.1007/978-981-99-9651-3_12(235-245)Online publication date: 16-Apr-2024
https://doi.org/10.1007/978-981-99-9651-3_12
Moti MSimatis PPapadias D(2022)WaffleProceedings of the VLDB Endowment10.14778/3574245.357425316:4(670-683)Online publication date: 1-Dec-2022
https://dl.acm.org/doi/10.14778/3574245.3574253
Zhu HLiu WYin JZheng LHuang XXu JLee W(2022)Continuous Geo-Social Group Monitoring in Dynamic LBSNsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.3218844(1-14)Online publication date: 2022
https://doi.org/10.1109/TKDE.2022.3218844
Zhu HLiu WYin JWang MXu JHuang XLee W(2022)Continuous Geo-Social Group Monitoring over Moving Users2022 IEEE 38th International Conference on Data Engineering (ICDE)10.1109/ICDE53745.2022.00028(312-324)Online publication date: May-2022
https://doi.org/10.1109/ICDE53745.2022.00028
Teng DNehe AEmanuel PBaig FKong JWang F(2021)GPU-based Real-time Contact Tracing at ScaleProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483627(1-10)Online publication date: 2-Nov-2021
https://dl.acm.org/doi/10.1145/3474717.3483627
Wu SPang ZChen GGao YZhao CXiang S(2021)NEIST: A Neural-Enhanced Index for Spatio-Temporal QueriesIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.294594733:4(1659-1673)Online publication date: 1-Apr-2021
https://doi.org/10.1109/TKDE.2019.2945947
Cazzolato MTraina ABöhm K(2021)Establishing trajectories of moving objects without identitiesInformation Systems10.1016/j.is.2021.101955105:COnline publication date: 29-Dec-2021
https://dl.acm.org/doi/10.1016/j.is.2021.101955
Li XMango JSong JZhang D(2021)XStar: a software system for handling taxi trajectory big dataComputational Urban Science10.1007/s43762-021-00015-w1:1Online publication date: 28-Jul-2021
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