- Title
- Business context in big data analytics
- Creator
- Dinh, Loan; Karmakar, Gour; Kamruzzaman, Joarder; Stranieri, Andrew
- Date
- 2015
- Type
- Text; Conference proceedings
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/101663
- Identifier
- vital:10676
- Identifier
- ISBN:9781467372183 (ISBN)
- Identifier
-
https://doi.org/10.1109/ICICS.2015.7459846
- 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. © 2015 IEEE.; 2015 10th International Conference on Information, Communications and Signal Processing, ICICS 2015
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 10th International Conference on Information, Communications and Signal Processing, ICICS 2015; Singapore; 2nd-4th December 2015
- Rights
- Copyright © 2015 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Big Data; Context Keyword; Hadoop; Query Context; Semantic Value; Sentiment Analysis; Significance Level; Data acquisition; Search engines; Semantics; Signal processing; Significance levels
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