Hardware Acceleration for Spatial Selections and Joins

Chengyu Sun, Divyakant Agrawal, Amr El Abbadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

38 Citations (Scopus)

Abstract

Spatial database operations are typically performed in two steps. In the filtering step, indexes and the minimum bounding rectangles (MBRs) of the objects are used to quickly determine a set of candidate objects, and in the refinement step, the actual geometries of the objects are retrieved and compared to the query geometry or each other. Because of the complexity of the computational geometry algorithms involved, the CPU cost of the refinement step is usually the dominant cost of the operation for complex geometries such as polygons. In this paper, we propose a novel approach to address this problem using efficient rendering and searching capabilities of modern graphics hardware. This approach does not require expensive pre-processing of the data or changes to existing storage and index structures, and it applies to both intersection and distance predicates. Our experiments with real world datasets show that by combining hardware and software methods, the overall computational cost can be reduced substantially for both spatial selections and joins.

Original languageEnglish
Title of host publicationProceedings of the ACM SIGMOD International Conference on Management of Data
EditorsA.Y. Halevy, Z.G. Ives, A.H. Doan
Pages455-466
Number of pages12
Publication statusPublished - 2003
Externally publishedYes
Event2003 ACM SIGMOD International Conference on Management of Data - San Diego, CA, United States
Duration: 9 Jun 200312 Jun 2003

Other

Other2003 ACM SIGMOD International Conference on Management of Data
CountryUnited States
CitySan Diego, CA
Period9/6/0312/6/03

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Keywords

  • Hardware acceleration
  • Spatial join
  • Spatial selection

ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Sun, C., Agrawal, D., & El Abbadi, A. (2003). Hardware Acceleration for Spatial Selections and Joins. In A. Y. Halevy, Z. G. Ives, & A. H. Doan (Eds.), Proceedings of the ACM SIGMOD International Conference on Management of Data (pp. 455-466)