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Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases

Author

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  • Xin Liu
  • Yanju Zhou
  • Xiaohong Chen

Abstract

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.

Suggested Citation

  • Xin Liu & Yanju Zhou & Xiaohong Chen, 2018. "Mining Outlier Data in Mobile Internet-Based Large Real-Time Databases," Complexity, Hindawi, vol. 2018, pages 1-12, January.
  • Handle: RePEc:hin:complx:9702304
    DOI: 10.1155/2018/9702304
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    1. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.
    2. Zeng, Yaohui & Zhang, Zijun & Kusiak, Andrew, 2015. "Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms," Energy, Elsevier, vol. 86(C), pages 393-402.
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    Cited by:

    1. David Gil & Magnus Johnsson & Higinio Mora & Julian Szymański, 2019. "Review of the Complexity of Managing Big Data of the Internet of Things," Complexity, Hindawi, vol. 2019, pages 1-12, February.

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