CBFM:cutted Bloom filter matrix for multi-dimensional membership query
In order to improve the flexibility and accuracy of mu i-dimensional membership query,a new indexing structure called CBFM(cutted Bloom filter matrix)was proposed.CBFM built the bit matrix by the Cartesian product of different bloom filters,each representing one attribute of primary data.In this way...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2016-03-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016061/ |
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| author | Yong WANG Xiao-chun YUN ANGShu-peng WANG Xi WANG |
| author_facet | Yong WANG Xiao-chun YUN ANGShu-peng WANG Xi WANG |
| author_sort | Yong WANG |
| collection | DOAJ |
| description | In order to improve the flexibility and accuracy of mu i-dimensional membership query,a new indexing structure called CBFM(cutted Bloom filter matrix)was proposed.CBFM built the bit matrix by the Cartesian product of different bloom filters,each representing one attribute of primary data.In this way,the proposed matrix supported by-attribute membership query.Besides,the attribute combinations in the bit matrix could be reduced and weighted on demand to further enhance memory utilization rate.Theoretical analysis proves that CBFM utilizes memory more effi-ciently than BFM,the current state of art.Experiments also show that,on the scenario of memory size fixed,the false positive rate of CBFM is lower than that of all other indexin ethods.Especially on the scenario of memory constrained,the false positive rate of CBFM can be 3 orders of magnitude lower than that of BFM(Bloom filter matrix) indexing me-thod.CBFM is an accurate data structure for multi-dimensional membership query. |
| format | Article |
| id | doaj-art-faa59e50ab284646b2bc8a609f17cf35 |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2016-03-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-faa59e50ab284646b2bc8a609f17cf352025-01-14T06:55:05ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2016-03-013713914759699964CBFM:cutted Bloom filter matrix for multi-dimensional membership queryYong WANGXiao-chun YUNANGShu-peng WANGXi WANGIn order to improve the flexibility and accuracy of mu i-dimensional membership query,a new indexing structure called CBFM(cutted Bloom filter matrix)was proposed.CBFM built the bit matrix by the Cartesian product of different bloom filters,each representing one attribute of primary data.In this way,the proposed matrix supported by-attribute membership query.Besides,the attribute combinations in the bit matrix could be reduced and weighted on demand to further enhance memory utilization rate.Theoretical analysis proves that CBFM utilizes memory more effi-ciently than BFM,the current state of art.Experiments also show that,on the scenario of memory size fixed,the false positive rate of CBFM is lower than that of all other indexin ethods.Especially on the scenario of memory constrained,the false positive rate of CBFM can be 3 orders of magnitude lower than that of BFM(Bloom filter matrix) indexing me-thod.CBFM is an accurate data structure for multi-dimensional membership query.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016061/query algorithmmulti-dimensional membership queryBloom filterbit matrix |
| spellingShingle | Yong WANG Xiao-chun YUN ANGShu-peng WANG Xi WANG CBFM:cutted Bloom filter matrix for multi-dimensional membership query Tongxin xuebao query algorithm multi-dimensional membership query Bloom filter bit matrix |
| title | CBFM:cutted Bloom filter matrix for multi-dimensional membership query |
| title_full | CBFM:cutted Bloom filter matrix for multi-dimensional membership query |
| title_fullStr | CBFM:cutted Bloom filter matrix for multi-dimensional membership query |
| title_full_unstemmed | CBFM:cutted Bloom filter matrix for multi-dimensional membership query |
| title_short | CBFM:cutted Bloom filter matrix for multi-dimensional membership query |
| title_sort | cbfm cutted bloom filter matrix for multi dimensional membership query |
| topic | query algorithm multi-dimensional membership query Bloom filter bit matrix |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2016061/ |
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