Approximate membership query algorithm for incomplete data based on Bloom filter
More and more scenarios require approximate membership queries for incomplete query data, but traditional Bloom filters for membership queries cannot meet these requirements. An approximate membership query algorithm for incomplete data based on Bloom filter is proposed. It first preprocesses the mi...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | zho |
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National Computer System Engineering Research Institute of China
2022-03-01
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| Series: | Dianzi Jishu Yingyong |
| Subjects: | |
| Online Access: | http://www.chinaaet.com/article/3000147058 |
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| author | Wu Jiawen Wang Yuke Pei Shuyu Xie Kun Liu Chuda |
| author_facet | Wu Jiawen Wang Yuke Pei Shuyu Xie Kun Liu Chuda |
| author_sort | Wu Jiawen |
| collection | DOAJ |
| description | More and more scenarios require approximate membership queries for incomplete query data, but traditional Bloom filters for membership queries cannot meet these requirements. An approximate membership query algorithm for incomplete data based on Bloom filter is proposed. It first preprocesses the missing parts of the high-dimensional incomplete data, then converts the high-dimensional data to the low-dimensional data based on PCA technique, and the low-dimensional data is stored in a Bloom filter by combining local sensitive hash functions with standard hash functions. Extensive experiments are conducted using two publicly real-world network performance datasets, and it shows that the proposed algorithm efficiently solves the approximate membership query problem for data with incomplete data. It is also necessary to enrich the means of filling in the missing parts in the data pre-processing. The proposed solution can effectively solve the approximate membership query problem for data with missingness. |
| format | Article |
| id | doaj-art-05d2e6ae32d14c19821d036703303da1 |
| institution | Kabale University |
| issn | 0258-7998 |
| language | zho |
| publishDate | 2022-03-01 |
| publisher | National Computer System Engineering Research Institute of China |
| record_format | Article |
| series | Dianzi Jishu Yingyong |
| spelling | doaj-art-05d2e6ae32d14c19821d036703303da12025-08-20T03:29:43ZzhoNational Computer System Engineering Research Institute of ChinaDianzi Jishu Yingyong0258-79982022-03-01483788210.16157/j.issn.0258-7998.2124683000147058Approximate membership query algorithm for incomplete data based on Bloom filterWu Jiawen0Wang Yuke1Pei Shuyu2Xie Kun3Liu Chuda4College of Computer Science and Electronic Engineering，Hunan University，Changsha 410082，ChinaOffice of Information，Hunan University，Changsha 410082，ChinaCollege of Computer Science and Electronic Engineering，Hunan University，Changsha 410082，ChinaCollege of Computer Science and Electronic Engineering，Hunan University，Changsha 410082，ChinaChangsha Aeronautical Vocational and Technical College，Changsha 410082，ChinaMore and more scenarios require approximate membership queries for incomplete query data, but traditional Bloom filters for membership queries cannot meet these requirements. An approximate membership query algorithm for incomplete data based on Bloom filter is proposed. It first preprocesses the missing parts of the high-dimensional incomplete data, then converts the high-dimensional data to the low-dimensional data based on PCA technique, and the low-dimensional data is stored in a Bloom filter by combining local sensitive hash functions with standard hash functions. Extensive experiments are conducted using two publicly real-world network performance datasets, and it shows that the proposed algorithm efficiently solves the approximate membership query problem for data with incomplete data. It is also necessary to enrich the means of filling in the missing parts in the data pre-processing. The proposed solution can effectively solve the approximate membership query problem for data with missingness.http://www.chinaaet.com/article/3000147058bloom filterapproximate membership queryquery algorithm |
| spellingShingle | Wu Jiawen Wang Yuke Pei Shuyu Xie Kun Liu Chuda Approximate membership query algorithm for incomplete data based on Bloom filter Dianzi Jishu Yingyong bloom filter approximate membership query query algorithm |
| title | Approximate membership query algorithm for incomplete data based on Bloom filter |
| title_full | Approximate membership query algorithm for incomplete data based on Bloom filter |
| title_fullStr | Approximate membership query algorithm for incomplete data based on Bloom filter |
| title_full_unstemmed | Approximate membership query algorithm for incomplete data based on Bloom filter |
| title_short | Approximate membership query algorithm for incomplete data based on Bloom filter |
| title_sort | approximate membership query algorithm for incomplete data based on bloom filter |
| topic | bloom filter approximate membership query query algorithm |
| url | http://www.chinaaet.com/article/3000147058 |
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