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Introducing Elasticity for Spatial Knowledge Management

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  • David A. Gadish

    (California State University Los Angeles, USA)

Abstract

The internal validity of a spatial database can be discovered using the data contained within one or more databases. Spatial consistency includes topological consistency, or the conformance to topological rules. Discovery of inconsistencies in spatial data is an important step for improvement of spatial data quality as part of the knowledge management initiative. An approach for detecting topo-semantic inconsistencies in spatial data is presented. Inconsistencies between pairs of neighboring spatial objects are discovered by comparing relations between spatial objects to rules. A property of spatial objects, called elasticity, has been defined to measure the contribution of each of the objects to inconsistent behavior. Grouping of multiple objects, which are inconsistent with one another, based on their elasticity is proposed. The ability to discover groups of neighboring objects that are inconsistent with one another can serve as the basis of an effort to understand and increase the quality of spatial data sets. Elasticity should therefore be incorporated into knowledge management systems that handle spatial data.

Suggested Citation

  • David A. Gadish, 2008. "Introducing Elasticity for Spatial Knowledge Management," International Journal of Knowledge Management (IJKM), IGI Global, vol. 4(3), pages 64-81, July.
    • Handle: RePEc:igg:jkm000:v:4:y:2008:i:3:p:64-81
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