Kaleidomaps: A New Technique for the Visualization of Multivariate Time-Series Data

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.

Keywords

Keywords Kaleidomaps information visualization multivariate time-series data cyclic graphs data mining

Get full access to this article

View all access options for this article.

References

Tufte

. The Visual Display of Quantitative Information. Graphics Press: Cheshire, Connecticut, 1983.

Weber

Alexa

Muller

. Visualizing time-series on spirals. INFOVIS 1901, 2001 (San Diego, USA), IEEE Press: New York, 2001; 7–13.

Johnson

. Top scientific visualization research problems. IEEE Computer Graphics and Applications 2004; 24: 13–17.

Box

GEP

Jenkins

. Time Series Analysis: Forecasting and Control. 2nd edn. Holden Day: San Francisco, CA, 1976.

Evertsz

CJG

. Fractal geometry of financial time series. Fractals 3 1995; 3: 609–616.

Papinov

Miller

. Similarity search over time-series data using wavelets. ICDE 1902, 2002 (San Jose, USA), IEEE Press, New York, 2002; 212-ff.

Havre

Hetzler

Nowell

. ThemeRiver: Visualizing theme changes over time. INFOVIS1900, 2000 (Los Alamitos, USA), IEEE Press, New York, 2000; 115–123.

Van Wijk

Van Selow

. Cluster and calendar based visualization of time series data, INFOVIS 1999 (San Francisco, USA), IEEE Press, New York, 1999; 4–9.

Treinish

Silver

. Worm plots. IEEE Computer Graphics and Applications 1997; 7: 17–20.

10.

Inselberg

. Multidimensional Detective. INFOVIS 1997, 1997 (Phoenix, USA), IEEE Press, New York, 1997; 100–107.

11.

Wegenkittl

Loffelmann

Groller

. Visualizing the behavior of higher dimensional dynamical systems. Visualization 1997, 1997 (Phoenix, USA), IEEE Press, New York, 1997; 119-ff.

12.

Müller

Schumann

. Visualization methods for time-dependent data — an overview. WSC 1903, 2003 (New Orleans, USA), SCS Press, San Diego, 2003; 737–745.

13.

Glass

. Moiré effects from random dots. Nature 1969; 223: 578–580.

14.

Wilson

Wilkinson

. Detection of global structure in glass patterns: Implications for form vision. Vision Research 1998; 38: 2933–2947.

15.

Glass

Perez

. Perception of random dot interference patterns. Nature 1973; 246: 360–362.

16.

Carlis

Konstan

. Interactive visualization of serial periodic data. UIST 1998, 1998 (San Francisco, USA), ACM Press, New York, 1998; 29–38.

17.

Keim

. Designing pixel-orientated visualization techniques: Theory and applications. IEEE Transactions of Visualization and Computer Graphics 2000; 6: 59–78.

18.

Schneiderman

. The eyes have it: A task by data type taxonomy for information visualization. Visual Languages 1996, 1996 (Boulder, USA), IEEE Press, New York, 1996; 336–343.

19.

Buja

McDonald

Michalak

Stutzle

. Interactive data visualization using focus & linking. Visualization 1991, 1991 (Los Alamitos, USA), IEEE Press, New York, 1991; 156–163.

20.

Green

Swets

. Signal Detection Theory and Psychophysics. John Wiley and Sons: New York, 1966.

21.

Dark

. Pulsatile characteristics of the systemic arterial system during acute hemorrhagic hypovolemia. PhD Thesis, MRC Trauma Group, Manchester University, 2002.

22.

Codman . Johnson and Johnson Medical Limited, Berkshire, UK. [web document] http://www.codman.com (accessed 22nd May 2007).

23.

Deltex Medical Limited, Chichester, UK. [web document] http://www.deltexmedical.com (accessed 22nd May 2007).

24.

Hubbard

. The World According to Wavelets – The Story of a Mathematical Technique in the Making. A K Peters: Massachusetts, 1999.

25.

Aydin

Purdy

Dark

. Localizing systolic ejection within a trans-oesophageal CW Doppler ultrasound aortovelographic waveform. IEEE Engineering in Medicine and Biology 2002; 2: 1315–1316.

26.

Dark

Little

Purdy

Nirmalan

. Localizing ventricular systole improves trans-oesophageal Doppler derived surrogates of left ventricular filling status during acute preload stresses. British Journal of Anaesthesia 2004; 93: 313–314.

27.

Daubechies

. Ten Lectures on Wavelets. Society for Industrial & Applied Math: Philadelphia, 1992.

28.

The Air Quality Archive, The UK National Air Quality Information Archive [WWW Document] http://www.airquality.co.uk/archive/data_and_statistics_home.php (accessed 24 March 2005).