Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks
In order to further improve the technical level of data cleaning and data mining and better avoid the defects of uncertain knowledge expression in traditional Bayesian networks, a Bayesian network algorithm based on combined data cleaning and mining technology is proposed, and a manual functional da...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Advances in Multimedia |
| Online Access: | http://dx.doi.org/10.1155/2022/6731781 |
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| _version_ | 1850174040202805248 |
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| author | Suzhen Zhang Yuechun Wang Qing Lv |
| author_facet | Suzhen Zhang Yuechun Wang Qing Lv |
| author_sort | Suzhen Zhang |
| collection | DOAJ |
| description | In order to further improve the technical level of data cleaning and data mining and better avoid the defects of uncertain knowledge expression in traditional Bayesian networks, a Bayesian network algorithm based on combined data cleaning and mining technology is proposed, and a manual functional data cleaning architecture based on Hadoop is constructed. The results show that the traditional neighbor sorting algorithm with window size of 5 takes the least time to process the same amount of data. The nearest neighbor sorting algorithm with window size 7 is always the longest. The time consumption of the nonfixed window nearest neighbor sorting algorithm is similar to that of the traditional nearest neighbor sorting algorithm with a window size of 5. However, with the increase of data volume, the consumption time increases rapidly until it approaches the consumption time of the traditional sorting nearest neighbor algorithm with window size of 7. Therefore, the algorithm can improve the precision of data cleaning at the expense of cleaning speed, which proves that the artificial intelligence architecture based on combined data significantly improves the efficiency of the algorithm and can effectively analyze and process large data sets. |
| format | Article |
| id | doaj-art-c71d797e702b48e494eecb3f7f74aeb5 |
| institution | OA Journals |
| issn | 1687-5699 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Multimedia |
| spelling | doaj-art-c71d797e702b48e494eecb3f7f74aeb52025-08-20T02:19:44ZengWileyAdvances in Multimedia1687-56992022-01-01202210.1155/2022/6731781Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian NetworksSuzhen Zhang0Yuechun Wang1Qing Lv2Shijiazhuang Posts and Telecommunications Technical CollegeShijiazhuang Posts and Telecommunications Technical CollegeShijiazhuang Posts and Telecommunications Technical CollegeIn order to further improve the technical level of data cleaning and data mining and better avoid the defects of uncertain knowledge expression in traditional Bayesian networks, a Bayesian network algorithm based on combined data cleaning and mining technology is proposed, and a manual functional data cleaning architecture based on Hadoop is constructed. The results show that the traditional neighbor sorting algorithm with window size of 5 takes the least time to process the same amount of data. The nearest neighbor sorting algorithm with window size 7 is always the longest. The time consumption of the nonfixed window nearest neighbor sorting algorithm is similar to that of the traditional nearest neighbor sorting algorithm with a window size of 5. However, with the increase of data volume, the consumption time increases rapidly until it approaches the consumption time of the traditional sorting nearest neighbor algorithm with window size of 7. Therefore, the algorithm can improve the precision of data cleaning at the expense of cleaning speed, which proves that the artificial intelligence architecture based on combined data significantly improves the efficiency of the algorithm and can effectively analyze and process large data sets.http://dx.doi.org/10.1155/2022/6731781 |
| spellingShingle | Suzhen Zhang Yuechun Wang Qing Lv Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks Advances in Multimedia |
| title | Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks |
| title_full | Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks |
| title_fullStr | Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks |
| title_full_unstemmed | Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks |
| title_short | Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks |
| title_sort | exploring artificial intelligence architecture in data cleaning based on bayesian networks |
| url | http://dx.doi.org/10.1155/2022/6731781 |
| work_keys_str_mv | AT suzhenzhang exploringartificialintelligencearchitectureindatacleaningbasedonbayesiannetworks AT yuechunwang exploringartificialintelligencearchitectureindatacleaningbasedonbayesiannetworks AT qinglv exploringartificialintelligencearchitectureindatacleaningbasedonbayesiannetworks |