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International Conference on Web Information Systems Engineering

WISE 2016: Web Information Systems Engineering – WISE 2016 pp 405–413Cite as

A Strategy to Improve Accuracy of Multi-dimensional Feature Forecasting in Big Data Stream Computing Environments

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10041))

Abstract

High accuracy of multi-dimensional feature forecasting is very important for online scheduling in big data stream computing environments. Currently, most stream computing systems only consider historical features, with future features ignored. In this paper, a strategy to improve accuracy of multi-dimensional feature forecasting for online data stream is proposed. It includes the following contributions. (1) Profiling principles of accurate future feature forecasting objectives from multi-dimensional big data streams. (2) Extracting future features from multi-dimensional historical features of data stream via an improved hybrid IGA-BP (Immune Genetic Algorithm and Back Propagation) algorithm. (3) Evaluating accuracy of future feature forecasting and acceptable latency objectives in big data stream computing environments. Experimental results conclusively demonstrate the efficiency and effectiveness of the proposed strategy.

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Acknowledgment

This work is supported by the National Natural Science Foundation of China under Grant No. 61602428; the Fundamental Research Funds for the Central Universities under Grant No. 2652015338 and No. N130316001.

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Authors and Affiliations

  1. School of Information Engineering, China University of Geosciences, Beijing, 100083, People’s Republic of China

    Dawei Sun & Hao Tang

  2. School of Information Technology, Deakin University, Geelong, Victoria, 3216, Australia

    Shang Gao

  3. School of Computer Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Fengyun Li

Authors
  1. Dawei Sun
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  2. Hao Tang
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  3. Shang Gao
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  4. Fengyun Li
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Corresponding author

Correspondence to Dawei Sun .

Editor information

Editors and Affiliations

  1. Poznań University of Economics, Poznan, Poland

    Wojciech Cellary

  2. University of Minnesota, Minneapolis, Minnesota, USA

    Mohamed F. Mokbel

  3. Tsinghua University, Beijing, China

    Jianmin Wang

  4. Victoria University, Melbourne, Victoria, Australia

    Hua Wang

  5. Victoria University, Melbourne, Victoria, Australia

    Rui Zhou

  6. Victoria University, Melbourne, Victoria, Australia

    Yanchun Zhang

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© 2016 Springer International Publishing AG

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Sun, D., Tang, H., Gao, S., Li, F. (2016). A Strategy to Improve Accuracy of Multi-dimensional Feature Forecasting in Big Data Stream Computing Environments. In: Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2016. WISE 2016. Lecture Notes in Computer Science(), vol 10041. Springer, Cham. https://doi.org/10.1007/978-3-319-48740-3_30

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  • DOI: https://doi.org/10.1007/978-3-319-48740-3_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48739-7

  • Online ISBN: 978-3-319-48740-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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