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Exploring spatial datasets with histograms
Sun C., Bandi N., Agrawal D., El Abbadi A. Distributed and Parallel Databases20 (1):57-88,2006.Type:Article
Date Reviewed: Mar 1 2007

Spatial database management systems (for example, geographical information systems) often suffer from high query processing costs. With the recent availability of massive amounts of online spatial data, there are strong demands for the development of efficient techniques to browse large datasets, to summarize spatial characteristics, so that users can quickly identify relevant data among enormous amounts of available data resources. However, a set of spatial relations should be supported in browsing applications.

In this paper, a new spatial relation model has been introduced to explore new spatial relations (called level-2 relations) that need to be supported in browsing applications. The level-2 topological relations are contains, contained, overlap, disjoint, and equal. Based on the Euler histogram techniques, the authors present three efficient approximation algorithms with constant time complexity to calculate the level-2 spatial relations. The proposed algorithms have been evaluated on both synthetic and real datasets. The idea proposed here is sufficiently justified by theorems and corollaries that are proven in the paper. Moreover, the references used are adequate and closely related to the paper’s content.

This paper is of high interest for researchers working in database systems, since the proposed approximation algorithms are useful in query optimization for spatial database systems. Scientists with a strong mathematical background will be able to follow the paper. It could also serve as reading material on databases in graduate courses.

Reviewer:  George Pallis Review #: CR133989 (0802-0191)
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