BiVis showing a tweets data set (abscissa encodes time; ordinate the tweet rate, hue encodes approximation error (clik to enlarge).
Recent advances in information visualization have shown that building proper structures to allow efficient lookup in the data can reduce significantly the time to build graphical representation of very large data sets, when compared to the linear scanning of the data. We present BiVis, a visualization technique that shows how such techniques can be further improved to reach a rendering time compatible with continuous interaction. To do so, we turn the lookup into an anytime algorithm compatible with a progressive visualization: a visualization presenting an approximation of the data and an estimation of the error can be displayed almost instantaneously and refined in successive frames until the error converges to zero. We also leverage the spatial coherency of the navigation: during the interaction, the state of the (possibly partial) lookup for the previous frames is reused to bootstrap the lookup for the next frame despite the view change. We show that those techniques allow the interactive exploration of out-of-core time series consisting of billions of events on commodity computers.
The code will be available soon under the GPLv3.0 license.
A video demonstration is available [mov].
