Abstract
A Bloom filter has been widely utilized to represent a set of items because it is a simple space-efficient randomized data structure. In this paper, we propose a new structure to support the representation of items with multiple attributes based on Bloom filters. The structure is composed of Parallel Bloom Filters (PBF) and a hash table to support the accurate and efficient representation and query of items. The PBF is a counter-based matrix and consists of multiple submatrixes. Each submatrix can store one attribute of an item. The hash table as an auxiliary structure captures a verification value of an item, which can reflect the inherent dependency of all attributes for the item. Because the correct query of an item with multiple attributes becomes complicated, we use a two-step verification process to ensure the presence of a particular item to reduce false positive probability.
This work is partially supported by HK RGC CERG B-Q827 and POLYU A-PA2F, and by the National Basic Research 973 Program of China under Grant 2004CB318201.
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Hua, Y., Xiao, B. (2006). A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services. In: Robert, Y., Parashar, M., Badrinath, R., Prasanna, V.K. (eds) High Performance Computing - HiPC 2006. HiPC 2006. Lecture Notes in Computer Science, vol 4297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11945918_30
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DOI: https://doi.org/10.1007/11945918_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68039-0
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