Efficient update and retrieval of objects in a multiresolution geospatial database

Prasher, S. B. and Zhou, X. (2003). Efficient update and retrieval of objects in a multiresolution geospatial database. In: S. Nittel and D. Gunopulos, Proceedings of the Fifteenth International Conference on Scientific and Statistical Database Management. 15th International Conference on Scientific and Statistical Database Management, Cambridge, Massachusetts, (193-201). 9-11 July 2003.

Author Prasher, S. B.
Zhou, X.
Title of paper Efficient update and retrieval of objects in a multiresolution geospatial database
Conference name 15th International Conference on Scientific and Statistical Database Management
Conference location Cambridge, Massachusetts
Conference dates 9-11 July 2003
Proceedings title Proceedings of the Fifteenth International Conference on Scientific and Statistical Database Management
Journal name Ssdbm 2002: 15th International Conference On Scientific and Statistical Database Management
Place of Publication New Jersey, U.S.A.
Publisher The Institute of Electrical and Electronics Engineers
Publication Year 2003
Sub-type Fully published paper
ISBN 0-7695-1964-4
ISSN 1099-3371
1551-6393
Editor S. Nittel
D. Gunopulos
Volume 1
Start page 193
End page 201
Total pages 9
Collection year 2003
Language eng
Abstract/Summary Many emerging applications benefit from the extraction of geospatial data specified at different resolutions for viewing purposes. Data must also be topologically accurate and up-to-date as it often represents real-world changing phenomena. Current multiresolution schemes use complex opaque data types, which limit the capacity for in-database object manipulation. By using z-values and B+trees to support multiresolution retrieval, objects are fragmented in such a way that updates to objects or object parts are executed using standard SQL (Structured Query Language) statements as opposed to procedural functions. Our approach is compared to a current model, using complex data types indexed under a 3D (three-dimensional) R-tree, and shows better performance for retrieval over realistic window sizes and data loads. Updates with the R-tree are slower and preclude the feasibility of its use in time-critical applications whereas, predictably, projecting the issue to a one-dimensional index allows constant updates using z-values to be implemented more efficiently.
Subjects E1
280103 Information Storage, Retrieval and Management
700103 Information processing services
Q-Index Code E1

 
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Created: Fri, 24 Aug 2007, 12:40:21 EST