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Interactive visualizations can accelerate the data analysis loop throughnear-instantaneous feedback. To achieve interactivity, techniques such as datacubes and sampling are typically employed. While data cubes can speedupquerying for moderate-sized datasets, they are ineffective at doing so at alarger scales due to the size of the materialized data cubes. On the otherhand, while sampling can help scale to large datasets, it adds sampling errorand the associated issues into the process.  While increasing accuracy by looking at more data may sometimes be valuable,providing result minutiae might not be necessary if they do not impartadditional significant information. Indeed, such details not only incur ahigher \emph{computational} cost, but also tax the \emph{cognitive} load of theanalyst with worthless trivia. To reduce both the computational and cognitiveexpenses, we introduce \emph{InfiniViz}. Through a novel resultrefinement-based querying paradigm, \emph{InfiniViz} provides error-freeresults for large datasets by increasing bin resolutions progressively overtime. Through real and simulated workloads over real and benchmark datasets, weevaluate and demonstrate \emph{InfiniViz}'s utility at reducing both cognitiveand computational costs, while minimizing information loss.

InfiniViz: Interactive Visual Exploration using Progressive Bin Refinement