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Big Data pp 51–58Cite as

Big Data Analysis

Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we introduce the methods, architectures and tools for big data analysis. The analysis of big data mainly involves analytical methods for traditional data and big data, analytical architecture for big data, and software used for mining and analysis of big data. Data analysis is the final and the most important phase in the value chain of big data, with the purpose of extracting useful values, providing suggestions or decisions. Different levels of potential values can be generated through the analysis of datasets in different fields.

Keywords

  • Association Rule
  • Bloom Filter
  • Business Intelligence
  • Offline Analysis
  • Open Source Tool

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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  • DOI: 10.1007/978-3-319-06245-7_5
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References

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  3. What analytics, data mining, big data software you used in the past 12 months for a real project? http://www.kdnuggets.com/polls/2012/analytics-data-mining-big-data-software.html, 2012.

  4. Michael R Berthold, Nicolas Cebron, Fabian Dill, Thomas R Gabriel, Tobias Kötter, Thorsten Meinl, Peter Ohl, Christoph Sieb, Kilian Thiel, and Bernd Wiswedel. KNIME: The Konstanz information miner. Springer, 2008.

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Chen, M., Mao, S., Zhang, Y., Leung, V.C.M. (2014). Big Data Analysis . In: Big Data. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-06245-7_5

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  • DOI: https://doi.org/10.1007/978-3-319-06245-7_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06244-0

  • Online ISBN: 978-3-319-06245-7

  • eBook Packages: Computer ScienceComputer Science (R0)