Big data visual analytics: fundamentals, techniques, and tools

look of the structure → gain context understanding. Explore, filter out unimportant sections → deeper understanding of a sub section. Details of a particular focus point, show full detail when required. Interaction Types Projection Filtering Zooming Distortion Linking and brushing Challenge Interaction seems to be a difficult thing to pin down and characterise Let’s go back to the user trying to solve problems User-centred vs system-centred characterizations Interaction Types (cont.) Operator navigation, selection, manipulation, distortion, filtering Space of interaction screen, data value, data structure, attribute, object, visualization structure Parameters of the interaction operator focus, extents, transformation, blender (Ward, Grinstein, & Keim 10 ) The Interactive Visualisation Pipeline • Raw Data: idiosyncratic format • Data Tables: relations (case by variables)+ metadata • Visual Structures: spatial substrates + marks + graphical properties • Views: graphical parameters (position, scaling, clipping, etc.) Raw Data Data Tables Views Visual Structures Task Data Visual Form Data Transformations Visual Mappings View Transformations Human Interaction (Card, Mackinlay, Shneiderman 99) Interaction & Navigation Techniques Details Only? Filtering Zooming Focus+Context or Overview+Detail Multiple Views NOT Possible Filtering Reducing the amount of context in the display by querying or interaction Filter the information in the form of selecting a subset of the data along a range of numerical values in one or more dimensions. Only show convertible and hardtop Zooming (Cockburn 2009) “[...] temporal separation between views; users magnify [...] or demagnify [...] a dataset in place rather than seeing both views simultaneously.” -temporal separation