Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases
Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/9702304 |
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| author | Xin Liu Yanju Zhou Xiaohong Chen |
| author_facet | Xin Liu Yanju Zhou Xiaohong Chen |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | Mining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes. |
| format | Article |
| id | doaj-art-08f97108ef9246d59c6fd09acd8595ae |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-08f97108ef9246d59c6fd09acd8595ae2025-02-03T05:52:07ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/97023049702304Mining Outlier Data in Mobile Internet-Based Large Real-Time DatabasesXin Liu0Yanju Zhou1Xiaohong Chen2School of Business, Central South University of China, Changsha 410083, ChinaSchool of Business, Central South University of China, Changsha 410083, ChinaSchool of Business, Central South University of China, Changsha 410083, ChinaMining outlier data guarantees access security and data scheduling of parallel databases and maintains high-performance operation of real-time databases. Traditional mining methods generate abundant interference data with reduced accuracy, efficiency, and stability, causing severe deficiencies. This paper proposes a new mining outlier data method, which is used to analyze real-time data features, obtain magnitude spectra models of outlier data, establish a decisional-tree information chain transmission model for outlier data in mobile Internet, obtain the information flow of internal outlier data in the information chain of a large real-time database, and cluster data. Upon local characteristic time scale parameters of information flow, the phase position features of the outlier data before filtering are obtained; the decision-tree outlier-classification feature-filtering algorithm is adopted to acquire signals for analysis and instant amplitude and to achieve the phase-frequency characteristics of outlier data. Wavelet transform threshold denoising is combined with signal denoising to analyze data offset, to correct formed detection filter model, and to realize outlier data mining. The simulation suggests that the method detects the characteristic outlier data feature response distribution, reduces response time, iteration frequency, and mining error rate, improves mining adaptation and coverage, and shows good mining outcomes.http://dx.doi.org/10.1155/2018/9702304 |
| spellingShingle | Xin Liu Yanju Zhou Xiaohong Chen Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases Complexity |
| title | Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases |
| title_full | Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases |
| title_fullStr | Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases |
| title_full_unstemmed | Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases |
| title_short | Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases |
| title_sort | mining outlier data in mobile internet based large real time databases |
| url | http://dx.doi.org/10.1155/2018/9702304 |
| work_keys_str_mv | AT xinliu miningoutlierdatainmobileinternetbasedlargerealtimedatabases AT yanjuzhou miningoutlierdatainmobileinternetbasedlargerealtimedatabases AT xiaohongchen miningoutlierdatainmobileinternetbasedlargerealtimedatabases |