1. ¹Department of Computer Science, University of Hull, U.K.
2. ²Department of Psychology, University of Hull, U.K.
3. ³Faculty of Engineering, University of Bahcesehir, Turkey
4. ⁴Intensive Care Research Group, Hope Hospital/University of Manchester, U.K.

Correspondence: Kim Bale, Department of Computer Science, University of Hull, U.K. Tel: +44 0 1482 465285; Fax: +44 0 1482 466666; E-mail: k.bale@hull.ac.uk

Received 11 July 2006; Revised 19 March 2007; Accepted 30 April 2007; Published online 31 May 2007.

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.