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A strategy to improve accuracy of multi-dimensional feature forecasting in big data stream computing environments

Sun, Dawei, Tang, Hao, Gao, Shang and Li, Fengyun 2016, A strategy to improve accuracy of multi-dimensional feature forecasting in big data stream computing environments, in WISE 2016 : Proceedings of the 17th International Conference on Web Information Systems Engineering, Springer, Berlin, Germany, pp. 405-413, doi: 10.1007/978-3-319-48740-3_30.

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Title A strategy to improve accuracy of multi-dimensional feature forecasting in big data stream computing environments
Author(s) Sun, Dawei
Tang, Hao
Gao, ShangORCID iD for Gao, Shang orcid.org/0000-0002-2947-7780
Li, Fengyun
Conference name Web Information Systems Engineering. International Conference (17th : Shanghai, China)
Conference location Shanghai, China
Conference dates 8-10 Nov. 2016
Title of proceedings WISE 2016 : Proceedings of the 17th International Conference on Web Information Systems Engineering
Editor(s) Cellary, W.
Mokbel, M.F.
Wang, J.
Wang,, H.
Zhou,, R.
Zhang, Y.
Publication date 2016
Series Lecture notes in computer science
Conference series Web Information Systems Engineering International Conference
Start page 405
End page 413
Total pages 9
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) Big data
Data stream
Feature forecasting,
Multi-dimensional fea-tures,
hybrid IGA-BP
algorithm
Summary 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.
ISBN 9783319487397
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-48740-3_30
Field of Research 089999 Information and Computing Sciences not elsewhere classified
08 Information And Computing Sciences
Socio Economic Objective 890399 Information Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090729

Document type: Conference Paper
Collections: School of Information Technology
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