Content-Based Approach for Improving Bloom Filter Efficiency
Bloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7922 |
| _version_ | 1827735090727223296 |
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| author | Mohammed Alsuhaibani Rehan Ullah Khan Ali Mustafa Qamar Suliman A. Alsuhibany |
| author_facet | Mohammed Alsuhaibani Rehan Ullah Khan Ali Mustafa Qamar Suliman A. Alsuhibany |
| author_sort | Mohammed Alsuhaibani |
| collection | DOAJ |
| description | Bloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed probability of incorrectly identifying an element as being a member of the set, known as the false positive rate (FPR). However, traditional bloom filters suffer from a high FPR and extensive memory usage, which can lead to incorrect query results and a slow performance. Thus, this study indicates that a content-based strategy could be a practical solution for these challenges. Specifically, our approach requires less bloom filter storage, consequently decreasing the probability of false positives. The effectiveness of several hash functions on our strategy’s performance was also evaluated. Experimental evaluations demonstrated that the proposed strategy could potentially decrease false positives by a substantial margin of up to 79.83%. The use of size-based content bits significantly contributes to the decrease in the number of false positives as well. However, as the volume of content bits rises, the impact on time is not considerably noticeable. Moreover, the evidence suggests that the application of a singular approach leads to a more than 50% decrease in false positives. |
| first_indexed | 2024-03-11T01:46:30Z |
| format | Article |
| id | doaj.art-c9f8bc5d126d4530b9bdbc0663548bbf |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:46:30Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-c9f8bc5d126d4530b9bdbc0663548bbf2023-11-18T16:13:22ZengMDPI AGApplied Sciences2076-34172023-07-011313792210.3390/app13137922Content-Based Approach for Improving Bloom Filter EfficiencyMohammed Alsuhaibani0Rehan Ullah Khan1Ali Mustafa Qamar2Suliman A. Alsuhibany3Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaDepartment of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi ArabiaBloom filters are a type of data structure that is used to test whether or not an element is a member of a set. They are known for being space-efficient and are commonly employed in various applications, such as network routers, web browsers, and databases. These filters work by allowing a fixed probability of incorrectly identifying an element as being a member of the set, known as the false positive rate (FPR). However, traditional bloom filters suffer from a high FPR and extensive memory usage, which can lead to incorrect query results and a slow performance. Thus, this study indicates that a content-based strategy could be a practical solution for these challenges. Specifically, our approach requires less bloom filter storage, consequently decreasing the probability of false positives. The effectiveness of several hash functions on our strategy’s performance was also evaluated. Experimental evaluations demonstrated that the proposed strategy could potentially decrease false positives by a substantial margin of up to 79.83%. The use of size-based content bits significantly contributes to the decrease in the number of false positives as well. However, as the volume of content bits rises, the impact on time is not considerably noticeable. Moreover, the evidence suggests that the application of a singular approach leads to a more than 50% decrease in false positives.https://www.mdpi.com/2076-3417/13/13/7922big databloom filtercontent baseddata structurelarge datasettext processing |
| spellingShingle | Mohammed Alsuhaibani Rehan Ullah Khan Ali Mustafa Qamar Suliman A. Alsuhibany Content-Based Approach for Improving Bloom Filter Efficiency Applied Sciences big data bloom filter content based data structure large dataset text processing |
| title | Content-Based Approach for Improving Bloom Filter Efficiency |
| title_full | Content-Based Approach for Improving Bloom Filter Efficiency |
| title_fullStr | Content-Based Approach for Improving Bloom Filter Efficiency |
| title_full_unstemmed | Content-Based Approach for Improving Bloom Filter Efficiency |
| title_short | Content-Based Approach for Improving Bloom Filter Efficiency |
| title_sort | content based approach for improving bloom filter efficiency |
| topic | big data bloom filter content based data structure large dataset text processing |
| url | https://www.mdpi.com/2076-3417/13/13/7922 |
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