L-priorities Bloom Filter： A New Member of the Bloom Filter Family

A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multi...

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Bibliographic Details
Published inInternational journal of automation and computing Vol. 9; no. 2; pp. 171 - 176
Main Authors Hu, Huang-Shui, Zhao, Hong-Wei, Mi, Fei
Format Journal Article
LanguageEnglish
Published Heidelberg Institute of Automation, Chinese Academy of Sciences 01.04.2012
Springer Nature B.V
College of Computer Science and Technology, Jilin University, Changchun 130012, PRC%College of Computer Science and Technology, Jilin University, Changchun 130012, PRC
Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education, Jilin University, Changchun 130012, PRC%Key Laboratory of Symbolic Computation and Knowledge Engineering for Ministry of Education, Jilin University, Changchun 130012, PRC
Subjects
CAE) and Design
Complexity
Computation
Computer Applications
Computer-Aided Engineering (CAD
Control
Data structures
Engineering
Expenses
Filter
L型
Mathematical analysis
Mechatronics
Priorities
Queries
Robotics
Vectors (mathematics)
假阳性率
家庭
数据结构
空间矩阵
费用查询
过滤器
time and space complexity
Bloom filter
bit space matrix
L-priorities
false positive
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Summary:A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.
Bibliography:Bloom filter, bit space matrix, false positive, L-priorities, time and space complexity.
A Bloom filter is a space-efficient data structure used for concisely representing a set as well as membership queries at the expense of introducing false positive. In this paper, we propose the L-priorities Bloom filter （LPBF） as a new member of the Bloom filter （BF） family, it uses a limited multidimensional bit space matrix to replace the bit vector of standard bloom filters in order to support different priorities for the elements of a set. We demonstrate the time and space complexity, especially the false positive rate of LPBF. Furthermore, we also present a detailed practical evaluation of the false positive rate achieved by LPBF. The results show that LPBF performs better than standard BFs with respect to false positive rate.
11-5350/TP
ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1476-8186
2153-182X
1751-8520
2153-1838
DOI:10.1007/s11633-012-0630-8