Abstract
Properly-designed bulk-loading techniques are more efficient than the conventional tuple-loading method in constructing a multidimensional index tree for a large data set. Although a number of bulk-loading algorithms have been proposed in the literature, most of them were designed for continuous data spaces (CDS) and cannot be directly applied to non-ordered discrete data spaces (NDDS). In this paper, we present a new space-partitioning-based bulk-loading algorithm for the NSP-tree — a multidimensional index tree recently developed for NDDSs . The algorithm constructs the target NSP-tree by repeatedly partitioning the underlying NDDS for a given data set until input vectors in every subspace can fit into a leaf node. Strategies to increase the efficiency of the algorithm, such as multi-way splitting, memory buffering and balanced space partitioning, are employed. Histograms that characterize the data distribution in a subspace are used to decide space partitions. Our experiments show that the proposed bulk-loading algorithm is more efficient than the tuple-loading algorithm and a popular generic bulk-loading algorithm that could be utilized to build the NSP-tree.
Research supported by US National Science Foundation (under grants # IIS-0414576 and # IIS-0414594), US National Institute of Health (under OK-INBRE Grant # P2PRR016478), The University of Michigan, and Michigan State University.
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Qian, G., Seok, HJ., Zhu, Q., Pramanik, S. (2008). Space-Partitioning-Based Bulk-Loading for the NSP-Tree in Non-ordered Discrete Data Spaces. In: Bhowmick, S.S., Küng, J., Wagner, R. (eds) Database and Expert Systems Applications. DEXA 2008. Lecture Notes in Computer Science, vol 5181. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85654-2_37
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DOI: https://doi.org/10.1007/978-3-540-85654-2_37
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