This paper investigates the processing of spatial queries with topological constraints, for which current database solutions are inappropriate. Topological relations, such as disjoint, meet, overlap, inside, and contains, have been well defined by the 9-intersection, a comprehensive model for binary topological relations. We focus on two types of queries: (1) “Which objects have a stated topological relation with a given spatial object?” and (2) “What is the topological relation between two given spatial objects?” Such queries are processed at two levels of detail. First, Minimum Bounding Rectangles are used as an approximation of the objects' geometry and as a means of identifying candidates that might satisfy the query. Next, the nine intersections that determine the topological relations between candidate pairs are calculated. We present algorithms for minimizing these computations. Considerable performance can be gained by exploiting the semantics of spatial relations. We also compare the approach for a naive cost model, which assumes that all relations have the same frequency of occurrence, with a refined cost model, which considers the probability of occurrence of the topological relations. The strategies presented here have three key benefits: (1) they are based on a well-defined formalism; (2) they are customizable; and (3) they can take into account important statistical information about the data.

Modelling Topological Spatial Relations: Strategies for Query Processing

CLEMENTINI, ELISEO;
1994

Abstract

This paper investigates the processing of spatial queries with topological constraints, for which current database solutions are inappropriate. Topological relations, such as disjoint, meet, overlap, inside, and contains, have been well defined by the 9-intersection, a comprehensive model for binary topological relations. We focus on two types of queries: (1) “Which objects have a stated topological relation with a given spatial object?” and (2) “What is the topological relation between two given spatial objects?” Such queries are processed at two levels of detail. First, Minimum Bounding Rectangles are used as an approximation of the objects' geometry and as a means of identifying candidates that might satisfy the query. Next, the nine intersections that determine the topological relations between candidate pairs are calculated. We present algorithms for minimizing these computations. Considerable performance can be gained by exploiting the semantics of spatial relations. We also compare the approach for a naive cost model, which assumes that all relations have the same frequency of occurrence, with a refined cost model, which considers the probability of occurrence of the topological relations. The strategies presented here have three key benefits: (1) they are based on a well-defined formalism; (2) they are customizable; and (3) they can take into account important statistical information about the data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11697/13734
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