A new indexing technique for supporting by-attribute membership query of multidimensional data

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, 7901 LNCS pp. 266 - 277
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Multidimensional Data indexing and lookup has been widely used in online data-intensive applications involving in data with multiple attributes. However, there remains a long way to go for the high performance multi-attribute data representation and lookup: the performance of index drops down with the increase of dimensions. In this paper, we present a novel data structure called Bloom Filter Matrix (BFM) to support multidimensional data indexing and by-attribute search. The proposed matrix is based on the Cartesian product of different bloom filters, each representing one attribute of the original data. The structure and parameter of each bloom filter is designed to fit the actual data characteristic and system demand, enabling fast object indexing and lookup, especially by-attribute search of multidimensional data. Experiments show that Bloom Filter Matrix is a fast and accurate data structure for multi-attribute data indexing and by-attribute search with high-correlated queries. © 2013 Springer-Verlag.
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