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Mining the Twitter Data Stream: A Review

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dc.contributor.author USMAN, MUHAMMAD
dc.contributor.author AKRAM SHAIKH, MUHAMMAD
dc.date.accessioned 2019-10-29T09:34:44Z
dc.date.available 2019-10-29T09:34:44Z
dc.date.issued 2018-01-01
dc.identifier.issn 2519-5404
dc.identifier.uri http://142.54.178.187:9060/xmlui/handle/123456789/735
dc.description.abstract Now a days, due to recent growth of the Internet and the World Wide Web large volume of data is available everywhere. Various approaches have been proposed in the literature to analyze such large volume data involving large number of dimensions. Real time data streams of large volume can be exploited for knowledge discovery. In the current study, a literature review of the different approaches adapted by various researchers to extract knowledge from Twitter data streams particularly by creating a multi-dimensional architecture comprising of Meta tags with tweets is conducted. These approaches have been reviewed in terms of data retrieval, storage, database design, analysis, visualization abilities and the technologies being used for these components. The study also discusses the technological gaps and proposes some recommendations for the future research. en_US
dc.language.iso en_US en_US
dc.publisher PASTIC en_US
dc.subject Twitter en_US
dc.subject Data Streams en_US
dc.subject Data Mining en_US
dc.subject Data Warehouse en_US
dc.subject Multidimensional Mining en_US
dc.subject PASTIC en_US
dc.title Mining the Twitter Data Stream: A Review en_US
dc.type Article en_US


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