A Multi-attribute Data Structure with Parallel Bloom Filters for Network Services

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.