Hiroshi Ishikawa's Social big data mining PDF

By Hiroshi Ishikawa

ISBN-10: 1498710948

ISBN-13: 9781498710947

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Extra info for Social big data mining

Example text

8 Distributed Parallel Computing Framework The computing framework for analyzing social big data consists of multiple layers. The technologies and tools used at each layer contain the following: The conceptual layer: This layer provides the big object model introduced in this chapter. 8 Interaction mining. Color image of this figure appears in the color plate section at the end of the book. The logical layer: This layer contains analytical tools such as multivariate analysis, data mining, machine learning, and natural language processing.

Is a hypothesis necessary in the first place? Indeed, without constructing a hypothesis beforehand, a certain kind of prediction may be attained by considering feature vectors of thousands of dimensions representing all the conceivable variables and feeding such vectors into machine learning Hypotheses in the Era of Big Data 47 or data analysis libraries runnable on Hadoop, which is a parallel software platform working on cluster computers. A case where a hypothesis-free method was a success has been reported by genome researchers.

F1: A Distributed SQL Database That Scales, Research, Google (2013). [Stonebraker et al. 2010] Michael Stonebraker, Daniel J. Abadi, David J. DeWitt, Samuel Madden, Erik Paulson, Andrew Pavlo and Alexander Rasin: MapReduce and parallel DBMSs: friends or foes? Communication ACM 53(1): 64–71 (2010). [Taboada et al. 2011] Maite Taboada, Julian Brooke, Milan Tofiloski, Kimberly Voll, Manfred Stede: Lexicon-Based Methods for Sentiment Analysis, MIT Computational Linguistics 37(2): 267–307 (2011). [Vogels 2007] W.

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Social big data mining by Hiroshi Ishikawa


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