Scalable-DSP: a high scalable distributed storage and processing system for unstructured data in big data environments Sun, Dawei and Gao, Shang 2017, Scalable-DSP: a high scalable distributed storage and processing system for unstructured data in big data environments, in Proceedings of the Australasian Computer Science Week Multiconference, ACM, New York, N.Y., pp. 1-5, doi: 10.1145/3014812.3014855. Attached Files Name Description MIMEType Size Downloads Title Scalable-DSP: a high scalable distributed storage and processing system for unstructured data in big data environments Author(s) Sun, Dawei Gao, Shang orcid.org/0000-0002-2947-7780 Conference name Australasian Computer Science Week. Multiconference (2017 : Geelong, Vic.) Conference location Geelong, Vic. Conference dates 2017/01/31 - 2017/02/03 Title of proceedings Proceedings of the Australasian Computer Science Week Multiconference Editor(s) [Unknown] Publication date 2017 Series ACM International Conference Proceeding Series Start page 1 End page 5 Total pages 5 Publisher ACM Place of publication New York, N.Y. Keyword(s) Big data Data storage Data processing Unstructured data Internet scale Scalability ISBN 9781450347686 Language eng DOI 10.1145/3014812.3014855 Field of Research 080599 Distributed Computing not elsewhere classified 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 ©2017, ACM Persistent URL http://hdl.handle.net/10536/DRO/DU:30092400 Document type: Conference Paper Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 29 Mar 2017, 08:13:30 EST Wed, 29 Mar 2017, 08:14:42 EST Wed, 29 Mar 2017, 11:05:38 EST Wed, 29 Mar 2017, 11:12:20 EST Thu, 24 May 2018, 15:25:04 EST Fri, 25 May 2018, 06:01:18 EST Fri, 25 May 2018, 11:54:20 EST Fri, 25 May 2018, 11:58:10 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 2 times in Scopus Search Google Scholar Access Statistics: 226 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Thu, 24 May 2018, 15:25:04 EST

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