16-04-2012, 05:13 PM
RANKING SPATIAL DATA BY QUALITY PREFERENCES
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INTRODUCTION
ABOUT THE PROJECT
SPATIAL database systems manage large collections of geographic entities, which apart from spatial attributes contain nonspatial information (e.g., name, size, type, price, etc.). In this paper, we study an interesting type of preference queries, which select the best spatial location with respect to the quality of facilities in its spatial neighborhood.
Given a set D of interesting objects (e.g., candidate locations), a top-k spatial preference query retrieves the k objects in D with the highest scores. The score of an object is defined by the quality of features (e.g., facilities or services) in its spatial neighborhood. As a motivating example, consider a real estate agency office that holds a database with available flats for lease. Here “feature” refers to a class of objects in a spatial map such as specific facilities or services.
. SYSTEM ANALYSIS
Existing System:
To our knowledge, there is no existing efficient solution for processing the top-k spatial preference query. A brute force approach for evaluating it is to compute the scores of all objects in D and select the top-k ones. This method, however, is expected to be very expensive for large input data sets.
Proposed System:
In this paper, we propose alternative techniques that aim at minimizing the I/O accesses to the object and feature data sets, while being also computationally efficient. Our techniques apply on spatial-partitioning access methods and compute upper score bounds for the objects indexed by them, which are used to effectively prune the search space.
SYSTEM DESIGN
Design is concerned with identifying software components specifying relationships among components. Specifying software structure and providing blue print for the document phase. Modularity is one of the desirable properties of large systems. It implies that the system is divided into several parts.
CONCLUSION:-
In this paper, we studied top-k spatial preference queries, which provide a novel type of ranking for spatial objects based on qualities of features in their neighborhood. The neighborhood of an object p is captured by the scoring function: 1) the range score restricts the neighborhood to a crisp region centered at p, whereas 2) the influence score relaxes the neighborhood to the whole space and assigns higher weights to locations closer to p. We presented five algorithms for processing top-k spatial preference queries. The baseline algorithm SP computes the scores of every object by querying on feature data sets. The algorithm GP is a variant of SP that reduces I/O cost by computing scores of objects in the same leaf node concurrently.