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<p><b>Abstract</b>—Selection and join queries are fundamental operations in Data Base Management Systems (DBMS). Support for nontraditional data, including spatial objects, in an efficient manner is of ongoing interest in database research. Toward this goal, access methods and cost models for spatial queries are necessary tools for spatial query processing and optimization. In this paper, we present analytical models that estimate the cost (in terms of node and disk accesses) of selection and join queries using R-tree-based structures. The proposed formulae need no knowledge of the underlying R-tree structure(s) and are applicable to uniform-like and nonuniform data distributions. In addition, experimental results are presented which show the accuracy of the analytical estimations when compared to actual runs on both synthetic and real data sets.</p>
Spatial databases, access methods, query optimization, cost models, R-trees.

E. Stefanakis, T. Sellis and Y. Theodoridis, "Efficient Cost Models for Spatial Queries Using R-Trees," in IEEE Transactions on Knowledge & Data Engineering, vol. 12, no. , pp. 19-32, 2000.
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