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Visualization Techniques

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Encyclopedia of Big Data Technologies

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Synonyms

Exploratory Data Analytics; Visual Data Exploration

Definitions

Visualization techniques for big data exploration, also known as visual data exploration (both terms will be used interchangeably in this article.), refer to a particular field of data visualization research that aims at devising and employing visualization techniques to aid the understanding of (extremely) large data sets (Keim 2001). These techniques evolved from the original concept of data visualization, which was originally defined as “the use of computer-supported, interactive, visual representations of abstract data to amplify cognition” (Card et al. 1999). The goal is thus to make information ready at hand so that people can make informed decisions in a timely fashion. The use of data visualization is often divided into two basic tasks (and tools thereof): data exploration and data representation. In data exploration, users are primarily concerned with investigating a data set in order to manipulate the...

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Authors and Affiliations

  1. IBM Research, São Paulo, Rua Tutóia 1157, Brazil

    Rogério Abreu de Paula

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  1. Rogério Abreu de Paula
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Correspondence to Rogério Abreu de Paula .

Editor information

Editors and Affiliations

  1. School of Comp. Sci. and Engineering, University of New South Wales School of Comp. Sci. and Engineering, Eveleigh, New South Wales, Australia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. School of Computing, Engineering and Mathematics, Western Sydney University, Penrith, NSW, Australia

    Rodrigo N. Calheiros

  2. Inria, LIP, ENS Lyon, 46 allee d'Italie, 69364, Lyon, France

    Marcos Dias de Assuncao

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de Paula, R.A. (2018). Visualization Techniques. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_84-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_84-1

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