A data as a product model for future consumption of big stream data in clouds

Huang, Guangyan, He, Jing, Chi, Chi-Hung, Zhou, Wanlei and Zhang, Yanchun 2015, A data as a product model for future consumption of big stream data in clouds, in SCC 2015: Proceedings of the 12th International Conference on Services Computing, IEEE, Piscataway, N.J., pp. 256-263, doi: 10.1109/SCC.2015.43.

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Title A data as a product model for future consumption of big stream data in clouds
Author(s) Huang, GuangyanORCID iD for Huang, Guangyan orcid.org/0000-0002-1821-8644
He, Jing
Chi, Chi-Hung
Zhou, WanleiORCID iD for Zhou, Wanlei orcid.org/0000-0002-1680-2521
Zhang, Yanchun
Conference name International Conference on Services Computing (12th : 2015 : New York, New York)
Conference location New York, New York
Conference dates 27 Jun. - 2 Jul. 2015
Title of proceedings SCC 2015: Proceedings of the 12th International Conference on Services Computing
Editor(s) Maglio, Paul
Paik, Incheon
Chou, Wu
Publication date 2015
Start page 256
End page 263
Total pages 8
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) data as a product
big data
big data, data consumption
cloud computing
Summary Data is becoming the world’s new natural resourceand big data use grows quickly. The trend of computingtechnology is that everything is merged into the Internet and‘big data’ are integrated to comprise completeinformation for collective intelligence. With the increasingsize of big data, refining big data themselves to reduce data sizewhile keeping critical data (or useful information) is a newapproach direction. In this paper, we provide a novel dataconsumption model, which separates the consumption of datafrom the raw data, and thus enable cloud computing for bigdata applications. We define a new Data-as-a-Product (DaaP)concept; a data product is a small sized summary of theoriginal data and can directly answer users’ queries. Thus, weseparate the mining of big data into two classes of processingmodules: the refine modules to change raw big data into smallsizeddata products, and application-oriented mining modulesto discover desired knowledge further for applications fromwell-defined data products. Our practices of mining big streamdata, including medical sensor stream data, streams of textdata and trajectory data, demonstrated the efficiency andprecision of our DaaP model for answering users’ queries
ISBN 9781467372817
Language eng
DOI 10.1109/SCC.2015.43
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 810105 Intelligence
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Grant ID DP140100841
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30081395

Document type: Conference Paper
Collections: School of Information Technology
2018 ERA Submission
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