Article

Location-based spatial queries

Authors:

Dimitris Papadias,

Dik Lun LeeAuthors Info & Claims

SIGMOD '03: Proceedings of the 2003 ACM SIGMOD international conference on Management of data

Pages 443 - 454

https://doi.org/10.1145/872757.872812

Published: 09 June 2003 Publication History

Abstract

In this paper we propose an approach that enables mobile clients to determine the validity of previous queries based on their current locations. In order to make this possible, the server returns in addition to the query result, a validity region around the client's location within which the result remains the same. We focus on two of the most common spatial query types, namely nearest neighbor and window queries, define the validity region in each case and propose the corresponding query processing algorithms. In addition, we provide analytical models for estimating the expected size of the validity region. Our techniques can significantly reduce the number of queries issued to the server, while introducing minimal computational and network overhead compared to traditional spatial queries.

References

[1]

{A91} Aurenhammer, F. Voronoi Diagrams: A Survey of a Fundamental Geometric Data Structure. ACM Computing Surveys, Vol 23(3), 345--405, 1991.]]

Digital Library

[2]

{APR99} Acharya, S., Poosala, V., Ramaswamy, S. Selectivity Estimation in Spatial Databases. SIGMOD, 1999]]

Digital Library

[3]

{B00} Bohm, C. A Cost Model for Query Processing in High Dimensional Data Spaces. ACM TODS, Vol. 25(2), pp. 129--178, 2000.]]

Digital Library

[4]

{BKSS90} Beckmann, N., Kriegel, H. P., Schneider, R., Seeger, B. The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. SIGMOD, 1990.]]

Digital Library

[5]

{BBKK97} Berchtold, S., Bohm, C., Keim, D.A., Kriegel, H. A Cost Model for Nearest Neighbor Search in High-Dimensional Data Space. PODS, 1997.]]

Digital Library

[6]

{BBK+01} Berchtold, S., Bohm, C., Keim, D., Krebs, F., Kriegel, H.P. On Optimizing Nearest Neighbor Queries in High-Dimensional Data Spaces. ICDT, 2001.]]

Digital Library

[7]

{BJKS02} Benetis, R., Jensen, C., Karciauskas, G., Saltenis, S. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects. IDEAS, 2002.]]

Digital Library

[8]

{BKSS90} Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B. The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. SIGMOD, 1990.]]

Digital Library

[9]

{BKOS97} de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O. Computational Geometry. pp. 145--161. Springer, 1997.]]

[10]

{GG98} Gaede, V., Günther, O. Multidimensional Access Methods. ACM Computing Surveys, 30(2), 1998.]]

Digital Library

[11]

{HS99} Samet, H., Hjaltason, G. Distance Browsing in Spatial Databases. ACM TODS, 1999.]]

Digital Library

[12]

{OBSC00} Okabe, A., Boots, B., Sugihara, K., Chiu, S. Spatial Tessellations: Concepts and Applications of Voronoi Diagrams. pp. 291--410. John Wiley, 2000.]]

Digital Library

[13]

{RKV95} Roussopoulos, N., Kelly, S., Vincent, F. Nearest Neighbor Queries. SIGMOD, 1995.]]

Digital Library

[14]

{SR01} Song, Z., Roussopoulos, N. K-Nearest Neighbor Search for Moving Query Point. SSTD, 2001.]]

Digital Library

[15]

{TP02} Tao, Y., Papadias, D. Time Parameterized Queries in Spatio-Temporal Databases. SIGMOD, 2002.]]

Digital Library

[16]

{TPS02} Tao, Y., Papadias, D., Shen, Q. Continuous Nearest Neighbor Search. VLDB, 2002.]]

Digital Library

[17]

{TSS00} Theodoridis, Y., Stefanakis, E., Sellis, T. Efficient Cost Models for Spatial Queries Using R-trees. TKDE, 12(1):19--32, 2000.]]

Digital Library

[18]

{Web} dias.cti.gr/~ytheod/research/datasets/ spatial.html]]

[19]

{WSB98} Weber, R., Schek, H.J., Blott, S. A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces. VLDB, 1998.]]

Digital Library

[20]

{ZL01} Zheng, B., Lee, D. Semantic Caching in Location-Dependent Query Processing. SSTD, 2001.]]

Digital Library

Cited By

Zhu RWang BYang XZheng B(2023)Closest Pairs Search Over Data StreamProceedings of the ACM on Management of Data10.1145/36173261:3(1-26)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617326
Chaves Carniel A(2023)Defining and designing spatial queries: the role of spatial relationshipsGeo-spatial Information Science10.1080/10095020.2022.216392427:6(1868-1892)Online publication date: 17-May-2023
https://doi.org/10.1080/10095020.2022.2163924
Köppl D(2022)Inferring Spatial Distance Rankings with Partial Knowledge on Routing NetworksInformation10.3390/info1304016813:4(168)Online publication date: 25-Mar-2022
https://doi.org/10.3390/info13040168
Show More Cited By

Index Terms

Location-based spatial queries

Recommendations

Location-dependent spatial query containment

Nowadays, location-related information is highly accessible to mobile users via issuing Location-Dependent Spatial Queries (LDSQs) with respect to their locations wirelessly to Location-Based Service (LBS) servers. Due to the limited mobile device ...
Optimal network location queries
GIS '10: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems

Given a set S of sites and a set O of weighted objects, an optimal location query finds the location(s) where introducing a new site maximizes the total weight of the objects that are closer to the new site than to any other site. With such a query, for ...
Evaluating uncertain location-based spatial queries
SAC '08: Proceedings of the 2008 ACM symposium on Applied computing

Location-Based Spatial Queries (LBSQ) are becoming more and more useful in location-based services such as those provided by cell-phones, Wireless LAN and GPS technologies. They support the access to information resources by taking into account the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGMOD '03: Proceedings of the 2003 ACM SIGMOD international conference on Management of data

June 2003

702 pages

ISBN:158113634X

DOI:10.1145/872757

Conference Chair:
Zachary Ives
University of Pennsylvania
,
General Chair:
Yannis Papakonstantinou
University of California, San Diego
,
Program Chair:
Alon Halevy
University of Washington

Copyright © 2003 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]

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 June 2003

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Conference

SIGMOD/PODS03

Sponsor:

SIGMOD

SIGMOD/PODS03: International Conference on Management of Data and Symposium on Principles Database and Systems

June 9 - 12, 2003

California, San Diego

Acceptance Rates

SIGMOD '03 Paper Acceptance Rate 53 of 342 submissions, 15%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

250
Total Citations
View Citations
1,878
Total Downloads

Downloads (Last 12 months)35
Downloads (Last 6 weeks)4

Reflects downloads up to 29 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Zhu RWang BYang XZheng B(2023)Closest Pairs Search Over Data StreamProceedings of the ACM on Management of Data10.1145/36173261:3(1-26)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3617326
Chaves Carniel A(2023)Defining and designing spatial queries: the role of spatial relationshipsGeo-spatial Information Science10.1080/10095020.2022.216392427:6(1868-1892)Online publication date: 17-May-2023
https://doi.org/10.1080/10095020.2022.2163924
Köppl D(2022)Inferring Spatial Distance Rankings with Partial Knowledge on Routing NetworksInformation10.3390/info1304016813:4(168)Online publication date: 25-Mar-2022
https://doi.org/10.3390/info13040168
Farooq MRabbani TAlvi ANaem M(2022)Pervasive Computing Issues, Vision an Exclusive Relationship Of Pervasive And Cloud ComputingVFAST Transactions on Software Engineering10.21015/vtse.v10i2.97010:2(09-16)Online publication date: 13-May-2022
https://doi.org/10.21015/vtse.v10i2.970
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
Bhandari AHasanov AAttique MCho HChung T(2021)Efficient Processing of All Nearest Neighbor Queries in Dynamic Road NetworksMathematics10.3390/math91011379:10(1137)Online publication date: 17-May-2021
https://doi.org/10.3390/math9101137
Alvin MAdhinugraha KAlamri SMir U(2021)Influence zone expansion for reverse k nearest neighbours queryMultimedia Tools and Applications10.1007/s11042-021-11275-383:5(15253-15266)Online publication date: 10-Aug-2021
https://doi.org/10.1007/s11042-021-11275-3
Hidayat ACheema MLin XZhang WZhang Y(2021)Continuous monitoring of moving skyline and top-k queriesThe VLDB Journal10.1007/s00778-021-00702-431:3(459-482)Online publication date: 11-Oct-2021
https://doi.org/10.1007/s00778-021-00702-4
Jiang WLi GAn JSun YChen HLi X(2021)Research on Indexing and KNN Query of Moving Objects in Road Network EnvironmentCommunications, Signal Processing, and Systems10.1007/978-981-15-8411-4_256(1944-1950)Online publication date: 8-Jun-2021
https://doi.org/10.1007/978-981-15-8411-4_256
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
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten