Data analysts must navigate increasingly large data sets, sometimes with billions or more records. I present methods for interactive visualization of big data, following the idea that perceptual and interactive scalability should be limited by the chosen resolution of the information, not the scale of the data.
I first describe methods for interactive querying using binned plots through a combination of multivariate data tiles and parallel query processing.
I then present a design space of scalable visual summaries that use data reduction methods (such as binned aggregation or sampling) to visualize a variety of data types.
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