Space-Partitioning-Based Bulk-Loading for the NSP-Tree in Non-ordered Discrete Data Spaces

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 bulkloading 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 t...

• Type: Conference
• Year: 2008
• Where: DEXA
• Authors: Gang Qian, Hyun-Jeong Seok, Qiang Zhu, Sakti Pramanik
• Keywords: Algorithm, Bulk-loading Algorithm, Database, DEXA 2008, Multidimensional Index Tree