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The need for good visualization is increasing, as data volume and complexity expand. In order to work with high volumes of structured and unstructured data, visualizations, supporting the ability of humans to make perceptual inferences, are of the utmost importance. In this regard, a lot of interactive visualization techniques have been developed in recent years. However, little emphasis has been placed on the evaluation of their usability and, in particular, on design characteristics. This paper contributes to closing this research gap by measuring the effects of appropriate visualization use based on data and task characteristics. Further, we specifically test the feature of interaction as it has been said to be an essential component of Big Data visualizations but scarcely isolated as an independent variable in experimental research. Data collection for the large-scale quantitative experiment was done using crowdsourcing (Amazon Mechanical Turk). The results indicate that both, choosing an appropriate visualization based on task characteristics and using the feature of interaction, increase usability considerably.

Does design matter when visualizing Big Data? An empirical study to investigate the effect of visualization type and interaction use