Comparative Evaluation of an Interactive Time-Series Visualizations that Combines Quantitative Data with Qualitative Abstractions

Authors  Wolfgang Aigner
Alexander Rind
Stephan Hoffmann

Institute of Software Technology & Interactive Systems, Vienna University of Technology, Austria

Abstract  In many application areas, analysts have to make sense of large volumes of multivariate time-series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain-specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency are available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well-known KNAVE-II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color-coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.
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Images  SemTimeZoom visualization prototype used in the empirical evaluation:

SemTimeZoom technique in experiment mode

The comparison technique is based on the visualization that is used in the KNAVE project.

Comparison visualization prototype used in the empirical evaluation:

Comparsion technique in experiment mode

Funding  This work was supported by the Centre for Visual Analytics Science and Technology CVAST (funded by the Austrian Federal Ministry of Economy, Family and Youth in the Laura Bassi Centres of Excellence initiative). Many thanks to Natalia and Gennady Andrienko for their help in task categorization, Silvia Miksch for her support, and Theresia Gschwandtner for reviewing our manuscript.