Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases
Joint Authors
Liu, Xin
Chen, Xiaohong
Zhou, Yanju
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-01-10
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Liu, Xin& Zhou, Yanju& Chen, Xiaohong. 2018. Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases. Complexity،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136906
Modern Language Association (MLA)
Liu, Xin…[et al.]. Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases. Complexity No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1136906
American Medical Association (AMA)
Liu, Xin& Zhou, Yanju& Chen, Xiaohong. Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases. Complexity. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1136906
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1136906