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GIPSY: joining spatial datasets with contrasting density

Authors:

Mirjana Pavlovic,

Farhan Tauheed,

Anastasia AilamakitAuthors Info & Claims

SSDBM '13: Proceedings of the 25th International Conference on Scientific and Statistical Database Management

Article No.: 11, Pages 1 - 12

https://doi.org/10.1145/2484838.2484855

Published: 29 July 2013 Publication History

Abstract

Many scientific and geographical applications rely on the efficient execution of spatial joins. Past research has produced several efficient spatial join approaches and while each of them can join two datasets, the problem of efficiently joining two datasets with contrasting density, i.e., with the same spatial extent but with a wildly different number of spatial elements, has so far been overlooked. State-of-the-art data-oriented spatial join approaches (e.g., based on the R-Tree) suffer from degraded performance due to overlap, whereas space-oriented approaches excessively read data from disk.

In this paper we develop GIPSY, a novel approach for the spatial join of two datasets with contrasting density. GIPSY uses fine-grained data-oriented partitioning and thus only retrieves the data needed for the join. At the same time it avoids the overlap related problems associated with data-oriented partitioning by using a crawling approach, i.e., without using a hierarchical tree. Our experiments show that GIPSY outperforms state-of-the-art disk-based spatial join algorithms by a factor of 2 to 18 and is particularly efficient when joining a dense dataset with several sparse datasets.

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Cited By

Bouros PMamoulis N(2019)Spatial joinsSIGSPATIAL Special10.1145/3355491.335549411:1(13-21)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3355491.3355494
Aghajarian DPuri SPrasad SAli MNewsam SRenz MTrajcevski GRavada S(2016)GCMFProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2996913.2996982(1-10)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2996913.2996982

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cover image ACM Other conferences

SSDBM '13: Proceedings of the 25th International Conference on Scientific and Statistical Database Management

July 2013

401 pages

ISBN:9781450319218

DOI:10.1145/2484838

Copyright © 2013 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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New York, NY, United States

Publication History

Published: 29 July 2013

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SSDBM '13

SSDBM '13: Conference on Scientific and Statistical Database Management

July 29 - 31, 2013

Maryland, Baltimore, USA

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Cited By

Bouros PMamoulis N(2019)Spatial joinsSIGSPATIAL Special10.1145/3355491.335549411:1(13-21)Online publication date: 5-Aug-2019
https://dl.acm.org/doi/10.1145/3355491.3355494
Aghajarian DPuri SPrasad SAli MNewsam SRenz MTrajcevski GRavada S(2016)GCMFProceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems10.1145/2996913.2996982(1-10)Online publication date: 31-Oct-2016
https://dl.acm.org/doi/10.1145/2996913.2996982

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