Eric Perlman
Abstract
We demonstrate data indexing and query processing techniques that improve the efficiency of comparing, correlating, and joining data contained in non-convex regions. We use computational geometry techniques to automatically characterize the region of space from which data are drawn, partition the region based on that characterization, and create an index from the partitions. Our motivating application performs distributed data analysis queries among federated database sites that store scientific data sets from the Chesapeake Bay. Our preliminary findings indicate that these techniques often reduce the number of I/Os needed to serve a query by a factor of five---depending on the geometry of the query region.
- W. P. Ball et al. A prototype system for multi-disciplinary shared cyberinfrastructure---Chesapeake Bay Environmental Observatory (CBEO). To appear in Journal of Hydrological Engineering, Accepted 2007.Google Scholar
- Cgal, Computational Geometry Algorithms Library. http://www.cgal.org.Google Scholar
- L. P. Chew. Constrained Delaunay triangulations. Algorithmica, 4(1):97--108, 1989.Google ScholarDigital Library
- M. McAllister and J. Snoeyink. Medial axis generalization of river networks. CaGIS, 27(2):129--138, 2000.Google ScholarCross Ref
- B. G. Nickerson. Automated cartographic generalization for linear features. Cartographica, 25(3):15--66, 1988.Google ScholarCross Ref
- H. Samet. Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2006. Google ScholarDigital Library
Index Terms
- Organizing and indexing non-convex regions
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