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Kaleidomaps: a new technique for the visualization of multivariate time-series data.

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Title: Kaleidomaps: a new technique for the visualization of multivariate time-series data.
Authors: Kim KB Bale1, Paul PC Chapman1, Nick NB Barraclough2, Jon JP Purdy1, Nizamettin NA Aydin3, Paul PD Dark4
Source: Information Visualization. Summer2007, Vol. 6 Issue 2, p155-167. 13p.
Subjects: Data visualization, Multivariate analysis, Time series analysis, Data analysis, Pattern perception, Signal processing, Graph theory
Abstract: In this paper, we describe a new visualization technique that can facilitate our understanding and interpretation of large complex multivariate time-series data sets. ‘Kaleidomaps’ have been carefully developed taking into account research into how we perceive form and structure within Glass patterns. We have enhanced the classic cascade plot using the curvature of a line to alter the detection of possible periodic patterns within multivariate dual periodicity data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap may induce a motion signal within the brain of the observer facilitating the perception of patterns within the data. Kaleidomaps and our associated visualization tools alter the rapid identification of periodic patterns not only within their own variants but also across many different sets of variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide the user with a set of visualization tools that permit the combination of multivariate time-series data sets in their raw form and also with the results of mathematical analysis. In this paper, we provide two case study examples of how Kaleidomaps can be used to improve our understanding of large complex multivariate time dependent data.Information Visualization (2007) 6, 155–167; doi:10.1057/palgrave.ivs.9500154 [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:In this paper, we describe a new visualization technique that can facilitate our understanding and interpretation of large complex multivariate time-series data sets. ‘Kaleidomaps’ have been carefully developed taking into account research into how we perceive form and structure within Glass patterns. We have enhanced the classic cascade plot using the curvature of a line to alter the detection of possible periodic patterns within multivariate dual periodicity data sets. Similar to Glass patterns, the concentric nature of the Kaleidomap may induce a motion signal within the brain of the observer facilitating the perception of patterns within the data. Kaleidomaps and our associated visualization tools alter the rapid identification of periodic patterns not only within their own variants but also across many different sets of variants. By linking this technique with traditional line graphs and signal processing techniques, we are able to provide the user with a set of visualization tools that permit the combination of multivariate time-series data sets in their raw form and also with the results of mathematical analysis. In this paper, we provide two case study examples of how Kaleidomaps can be used to improve our understanding of large complex multivariate time dependent data.Information Visualization (2007) 6, 155–167; doi:10.1057/palgrave.ivs.9500154 [ABSTRACT FROM AUTHOR]
ISSN:14738716
DOI:10.1057/palgrave.ivs.9500154