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From Astrophysics to Unconventional Computation pp 87–102Cite as

Visual Analytics

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 35))

Abstract

We are in a data-driven era. Making sense of data is becoming increasingly important, and this is driving the need for systems that enable people to analyze and understand data. Visual analytics presents users with a visual representation of their data so that they make sense of it: reason about it, ask important questions, and interpret the answers to those questions. This paper shows how effective visual analytics systems combine information visualisation (to visually represent data) with machine learning (to project data to a low-dimensional space). The methods are illustrated with two real-world case studies in drug discovery and condition monitoring of helicopter airframes. This paper is not the final word on the subject, but it also points the way to future research directions.

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Notes

  1. 1.

    In the original French: Le doute n’est pas une état bien agréable, mais l’assurance est un état ridicule.

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Author information

Authors and Affiliations

  1. School of Engineering, University of Bristol, Clifton, Bristol, BS8 1UB, UK

    Ian T. Nabney

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  1. Ian T. Nabney
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Correspondence to Ian T. Nabney .

Editor information

Editors and Affiliations

  1. Unconventional Computing Laboratory, University of the West of England, Bristol, UK

    Andrew Adamatzky

  2. Joint Quantum Centre, Durham University, Durham, UK

    Vivien Kendon

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Nabney, I.T. (2020). Visual Analytics. In: Adamatzky, A., Kendon, V. (eds) From Astrophysics to Unconventional Computation. Emergence, Complexity and Computation, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-030-15792-0_4

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