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Data Visualization Techniques and Algorithms

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Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing

Part of the book series: Springer Tracts in Nature-Inspired Computing ((STNIC))

Abstract

Data visualization plays an important role in gaining insight from data. Generally, traditional methods are used to systematically create graphical formats of data attributes of either numeric or textual data. However, these traditional methods are very time-consuming computationally when they must display data points of big data sources. It is significant to explore new methods and algorithms that require less computational time while taking into consideration the volume of data attributes involved. In this chapter, the behavior of animals is explored to help create a method and an algorithm for data visualization suited for big data visualization.

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Author information

Authors and Affiliations

  1. ICT and Society Research Group, Durban University of Technology, Durban, South Africa

    Israel Edem Agbehadji

  2. Department of Informatics, University of Leicester, Leicester, England, UK

    Hongji Yang

Authors
  1. Israel Edem Agbehadji
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  2. Hongji Yang
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Corresponding author

Correspondence to Israel Edem Agbehadji .

Editor information

Editors and Affiliations

  1. University of Macau, Taipa, China

    Simon James Fong

  2. Durban University of Technology, Durban, South Africa

    Richard C. Millham

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Agbehadji, I.E., Yang, H. (2021). Data Visualization Techniques and Algorithms. In: Fong, S., Millham, R. (eds) Bio-inspired Algorithms for Data Streaming and Visualization, Big Data Management, and Fog Computing. Springer Tracts in Nature-Inspired Computing. Springer, Singapore. https://doi.org/10.1007/978-981-15-6695-0_10

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