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Principles of Data Visualization

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Part of the Synthesis Lectures on Data, Semantics, and Knowledge book series (SLDSK)

Abstract

Information visualization aims at visually representing different types of data (e.g., geographic, numerical, text, network) in order to enable and reinforce cognition. Information visualization offers intuitive ways for information perception and manipulation that essentially amplify the overall cognitive performance of information processing, especially for non-expert users. Visual analytics combines information visualization with data exploration capabilities. It enables users to explore and analyze unknown (in terms of semantics and structure) sets of information, discover hidden correlations and causalities, and make sense of data in ways that are not always possible with traditional quantitative data analysis and mining techniques. This is of great importance, especially given the massive volumes of digital information concerning nearly every aspect of human activity that are currently being produced and collected. The so-called Big Data era refers to this tremendous volume of information collected by digital means and analyzed to produce new knowledge in a plethora of scientific domains.

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  • DOI: 10.1007/978-3-031-79490-2_2
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Po, L., Bikakis, N., Desimoni, F., Papastefanatos, G. (2020). Principles of Data Visualization. In: Linked Data Visualization. Synthesis Lectures on Data, Semantics, and Knowledge. Springer, Cham. https://doi.org/10.1007/978-3-031-79490-2_2

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