research-article

Towards a Painless Index for Spatial Objects

Authors:

Martin Stradling,

Jin HuangAuthors Info & Claims

ACM Transactions on Database Systems (TODS), Volume 39, Issue 3

Article No.: 19, Pages 1 - 42

https://doi.org/10.1145/2629333

Published: 07 October 2014 Publication History

Abstract

Conventional spatial indexes, represented by the R-tree, employ multidimensional tree structures that are complicated and require enormous efforts to implement in a full-fledged database management system (DBMS). An alternative approach for supporting spatial queries is mapping-based indexing, which maps both data and queries into a one-dimensional space such that data can be indexed and queries can be processed through a one-dimensional indexing structure such as the B⁺. Mapping-based indexing requires implementing only a few mapping functions, incurring much less effort in implementation compared to conventional spatial index structures. Yet, a major concern about using mapping-based indexes is their lower efficiency than conventional tree structures.

In this article, we propose a mapping-based spatial indexing scheme called Size Separation Indexing (SSI). SSI is equipped with a suite of techniques including size separation, data distribution transformation, and more efficient mapping algorithms. These techniques overcome the drawbacks of existing mapping-based indexes and significantly improve the efficiency of query processing. We show through extensive experiments that, for window queries on spatial objects with nonzero extents, SSI has two orders of magnitude better performance than existing mapping-based indexes and competitive performance to the R-tree as a standalone implementation. We have also implemented SSI on top of two off-the-shelf DBMSs, PostgreSQL and a commercial platform, both having R-tree implementation. In this case, SSI is up to two orders of magnitude faster than their provided spatial indexes. Therefore, we achieve a spatial index more efficient than the R-tree in a DBMS implementation that is at the same time easy to implement. This result may upset a common perception that has existed for a long time in this area that the R-tree is the best choice for indexing spatial objects.

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Cited By

Abdullah-Al-Mamun AHaider CWang JAref W(2022)The “AI + R” - tree: An Instance-optimized R - tree2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00023(9-18)Online publication date: Jul-2022
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Index Terms

Towards a Painless Index for Spatial Objects
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Transactions on Database Systems

ACM Transactions on Database Systems Volume 39, Issue 3

September 2014

264 pages

ISSN:0362-5915

EISSN:1557-4644

DOI:10.1145/2676651

Editor:
Christian S. Jensen
Aalborg University, Denmark

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

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Publication History

Published: 07 October 2014

Accepted: 01 January 2014

Revised: 01 November 2013

Received: 01 April 2013

Published in TODS Volume 39, Issue 3

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Cited By

Abdullah-Al-Mamun AHaider CWang JAref W(2022)The “AI + R” - tree: An Instance-optimized R - tree2022 23rd IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM55031.2022.00023(9-18)Online publication date: Jul-2022
https://doi.org/10.1109/MDM55031.2022.00023
Qi JZhang R(2022)IndexingEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_217-2(1-6)Online publication date: 15-Jun-2022
https://doi.org/10.1007/978-3-319-63962-8_217-2
Zhou XWang XZhou YLin QZhao JMeng X(2021)RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management SystemRemote Sensing10.3390/rs1309181513:9(1815)Online publication date: 6-May-2021
https://doi.org/10.3390/rs13091815
Kalay M(2021) Novel approaches on bulk‐loading of large scale spatial datasets Concurrency and Computation: Practice and Experience10.1002/cpe.659634:9Online publication date: 10-Sep-2021
https://doi.org/10.1002/cpe.6596
Qi JLiu GJensen CKulik L(2020)Effectively learning spatial indicesProceedings of the VLDB Endowment10.14778/3407790.340782913:12(2341-2354)Online publication date: 14-Sep-2020
https://dl.acm.org/doi/10.14778/3407790.3407829
Yang YBai PGe NGao ZQiu X(2020)LAZY R-treeJournal of Information Science10.1177/016555151982861646:2(243-257)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.1177/0165551519828616
Li CWu ZWu PZhao Z(2019)An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer NetworksISPRS International Journal of Geo-Information10.3390/ijgi81105128:11(512)Online publication date: 12-Nov-2019
https://doi.org/10.3390/ijgi8110512
Gao XGao YZhu YChen G(2019)U2-TreeIEEE/ACM Transactions on Networking10.1109/TNET.2019.289100827:1(201-213)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TNET.2019.2891008
Gao YGao XZhu YChen G(2019)An efficient and scalable multi-dimensional indexing scheme for modular data centersData & Knowledge Engineering10.1016/j.datak.2019.101729123:COnline publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1016/j.datak.2019.101729
Qi JZhang R(2019)IndexingEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_217(1015-1019)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_217
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