Business context in big data analytics

Loan Thi Ngoc Dinh, Gour Karmakar, Joarder Kamruzzaman, Andrew Stranieri

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

4 Citations (Scopus)

Abstract

Big data are generated from a variety of sources having different representation forms and formats, it raises a research question as how important data relevant to a business context can be captured and analyzed more accurately to represent deep and relevant business insight. There is a number of existing big data analytic methods available in the literature that consider contextual information such as the context of a query and its users, the context of a query-driven recommendation system, etc. However, these methods still have many challenges and none of them has considered the context of a business in either data collection or analysis process. To address this research gap, we introduce a big data analytic technique which embeds a business context in terms of the significance level of a query into the bedrock of its data collection and analysis process. We implemented our proposed model under the framework of Hadoop considering the context of a grocery shop. The results exhibit that our method substantially increases the amount of data collection and their deep insight with an increase of the significance level value.

Original languageEnglish
Title of host publication2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages5
ISBN (Electronic)9781467372183, 9781467372176, 9781467372169
DOIs
Publication statusPublished - 2015
Externally publishedYes
EventInternational Conference on Information, Communications and Signal Processing 2015 - , Singapore
Duration: 2 Dec 20154 Dec 2015
Conference number: 10th
http://www.icics.org/2015/home.asp

Conference

ConferenceInternational Conference on Information, Communications and Signal Processing 2015
Abbreviated titleICICS 2015
CountrySingapore
Period2/12/154/12/15
Internet address

Keywords

  • Big Data
  • Context Keyword
  • Hadoop
  • Query Context
  • Semantic Value
  • Sentiment Analysis
  • Significance Level

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