There Goes the Neighborhood: Relational Algebra for Spatial Data Search
arXiv:cs/0408031
Abstract
We explored ways of doing spatial search within a relational database: (1) hierarchical triangular mesh (a tessellation of the sphere), (2) a zoned bucketing system, and (3) representing areas as disjunctive-normal form constraints. Each of these approaches has merits. They all allow efficient point-in-region queries. A relational representation for regions allows Boolean operations among them and allows quick tests for point-in-region, regions-containing-point, and region-overlap. The speed of these algorithms is much improved by a zone and multi-scale zone-pyramid scheme. The approach has the virtue that the zone mechanism works well on B-Trees native to all SQL systems and integrates naturally with current query optimizers - rather than requiring a new spatial access method and concomitant query optimizer extensions. Over the last 5 years, we have used these techniques extensively in our work on SkyServer.sdss.org, and SkyQuery.net.
Original at http://research.microsoft.com/research/pubs/view.aspx?tr_id=736
References in corpus (1)
Cited by in corpus (8)
- Indexing the Sphere with the Hierarchical Triangular Mesh
- When Database Systems Meet the Grid
- The Zones Algorithm for Finding Points-Near-a-Point or Cross-Matching Spatial Datasets
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- Using Table Valued Functions in SQL Server 2005 To Implement a Spatial Data Library
- Teaching an Old Elephant New Tricks
- Cross-Matching Multiple Spatial Observations and Dealing with Missing Data
- Cross-matching Engine for Incremental Photometric Sky Survey