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Research on Data Flow Partitioning Based on Dynamic Feature Extraction

  • Wei WangEmail author
  • Min Zhang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

With the rapid development of the Internet of things, social networks, and e-commerce, the era of big data has arrived. Although big data has great potential for many areas such as industry, education, and health care, getting valuable knowledge from big data can be a daunting task. Big data has the characteristics of high-speed change, and its content and distribution characteristics are in dynamic changes. Most current models are static learning models that do not support online updating, making it difficult to learn dynamically changing big data features in real time. In order to solve this problem, this paper proposed a method to support incremental recursive least squares (IRLS) regression parameter estimation and variable sliding window algorithm to analyze and judge the trends of dynamic characteristics of data streams, which can provide early warning, status assessment, and decision support for monitoring objects and improve the accuracy and adaptability of data flow classification. The real-time computational and analysis accuracy are obviously improved than the traditional algorithm, and the simulation results verify the effectiveness of the proposed algorithm.

Keywords

Trend analysis Dynamic data mining Incremental recursive least squares method Variable sliding window 

Notes

Acknowledgements

This paper is supported by Natural Youth Science Foundation of China (61501326, 61401310) and Natural Science Foundation of China (61271411). It also supported by Tianjin Research Program of Application Foundation and Advanced Technology (15JCZDJC31500) and Tianjin Science Foundation (16JCYBJC16500).

References

  1. 1.
    Wang C, Pang X, Lu Z, et al. Research on data flow classification based on dynamic feature extraction and neural network. Comput Syst Appl. 2010;30(6):1539–42.zbMATHGoogle Scholar
  2. 2.
    Koski A, Juhola M, Meriste M. Syntactic recognition of ECG signals by attributed finite automata. Pattern Recogn. 1995;28(12):1927–40.CrossRefGoogle Scholar
  3. 3.
    Shatkay H, Zdonik S. Approximate queries and representations for large data sequences. In: Proceedings of 12th IEEE international conference on data engineering. Washington: IEEE Computer Society; 1996. p. 546–53.Google Scholar
  4. 4.
    Keogh E, Chu S, Hart D, et al. Segmenting time series: a survey and novel approach. In: Proceedings of IEEE international conference on data mining. Los Jose: IEEE Computer Society; 2001. p. 289–96.Google Scholar
  5. 5.
    Sylvie C, Carlos GB, Cathering C, et al. Trends extraction and analysis for complex system monitoring and decision support. Eng Appl Artif Intell. 2005;18(1):21–36.CrossRefGoogle Scholar
  6. 6.
    Zhou Q, Wu T. Research and application of a data flow trend analysis method. Control Decis Mak. 2008;23(10):1182–5.Google Scholar
  7. 7.
    Zhou Q, Cluett W. Recursive identification of time-varying systems via incremental estimation. Automatica. 1996;32(10):1427–31.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Wireless Mobile Communications and Power TransmissionTianjin Normal UniversityTianjinChina

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