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Computational Collective Intelligence pp 285–294Cite as

A Proposal of a Big Web Data Application and Archive for the Distributed Data Processing with Apache Hadoop

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9330))

Abstract

In recent years, research on big data, data storage and other topics that represent innovations in the analytics field has become very popular. This paper describes a proposal of a big web data application and archive for the distributed data processing with Apache Hadoop, including the framework with selected methods, which can be used with this platform. It proposes a workflow to create a web content mining application and a big data archive, which uses modern technologies like Python, PHP, JavaScript, MySQL and cloud services. It also shows the overview about the architecture, methods and data structures used in the context of web mining, distributed processing and big data analytics.

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Author information

Authors and Affiliations

  1. University of Pardubice, Faculty of Economics and Administration, Pardubice, Czech Republic

    Martin Lnenicka, Jan Hovad & Jitka Komarkova

Authors
  1. Martin Lnenicka
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  2. Jan Hovad
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  3. Jitka Komarkova
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Corresponding author

Correspondence to Jitka Komarkova .

Editor information

Editors and Affiliations

  1. Universidad Complutense de Madrid, Madrid, Spain

    Manuel Núñez

  2. Wroclaw University of Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  3. Computer Science Department, Universidad Autónoma De Madrid, Madrid, Spain

    David Camacho

  4. Wroclaw University of Technology, Wroclaw, Poland

    Bogdan Trawiński

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Lnenicka, M., Hovad, J., Komarkova, J. (2015). A Proposal of a Big Web Data Application and Archive for the Distributed Data Processing with Apache Hadoop. In: Núñez, M., Nguyen, N., Camacho, D., Trawiński, B. (eds) Computational Collective Intelligence. Lecture Notes in Computer Science(), vol 9330. Springer, Cham. https://doi.org/10.1007/978-3-319-24306-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-24306-1_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24305-4

  • Online ISBN: 978-3-319-24306-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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