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

  • Yu Hua
  • Bin Xiao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4297)


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.


Hash Function Multiple Attribute Hash Table Intersection Operation Bloom Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yu Hua
    • 1
    • 2
  • Bin Xiao
    • 1
  1. 1.Department of ComputingHong Kong Polytechnic UniversityKowloonHong Kong
  2. 2.School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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