Spatial indexing is a well researched field that benefited computer science with many outstanding results. Our effort in this paper can be seen as revisiting some outstanding contributions to spatial indexing, questioning some paradigms, and designing an access method with globally improved performance characteristics. In particular, we argue that dynamic R-tree construction is a typical clustering problem which can be addressed by incorporating existing clustering algorithms. As a working example, we adopt the well-known k-means algorithm. Further, we study the effect of relaxing the "two-way" split procedure and propose a "multi-way" split, which inherently is supported by clustering techniques. We compare our clustering approach to two prominent examples of spatial access methods, the R-and the R *-tree. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Brakatsoulas, S., Pfoser, D., & Theodoridis, Y. (2002). Revisiting R-tree construction principles. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2435 LNCS, pp. 149–162). Springer Verlag. https://doi.org/10.1007/3-540-45710-0_13
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