Abstract
The R-tree [7] family is the most popular multi-dimensional index method. The R-tree, however, has overlaps among index entries and its index page fanout decreases rapidly as data dimension increases. Furthermore, the R-tree has poor concurrency performance. For frequent-update multi-dimensional point data sets, the hB-pi [5] tree is a better choice than the R*-tree. But the hB-pi tree (and all other kd-tree based access methods) indexes the whole space no matter whether or not there is any data in some sub-spaces. Indexing empty space (i.e., space without data inside) leads to unnecessary data page accesses which increase with growing dimension. This paper addresses this problem by proposing the hB-pi* tree, which efficiently indicates empty spaces and improves range query performances while preserving the hB-pi’s high fan-out and good concurrency. Our methods can be applied to any kd-tree based access methods, and our claims are supported by extensive experimental evaluation.
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Zhou, P., Salzberg, B. (2008). The hB-pi* Tree: An Optimized Comprehensive Access Method for Frequent-Update Multi-dimensional Point Data. In: Ludäscher, B., Mamoulis, N. (eds) Scientific and Statistical Database Management. SSDBM 2008. Lecture Notes in Computer Science, vol 5069. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69497-7_22
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DOI: https://doi.org/10.1007/978-3-540-69497-7_22
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