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Conference Papers Year : 2015

ProgressiVis: a Toolkit for Steerable Progressive Analytics and Visualization

Jean-Daniel Fekete


ProgressiVis is a Python toolkit that implements a new programming paradigm that we call Progressive Analytics aimed at performing analytics in a progressive way. It allows analysts to see the progress of their analysis and to steer it while the computation is being done. While there have been a lot of articles arguing for introducing progressive visualization for visual analytics, ProgressiVis's novel computation paradigm allows the practical implementation of progressive visualization and computation in a general way with a reasonable complexity for the programmer. Instead of running algorithms to completion one after the other, as done in all existing scientific analysis systems, ProgressiVis modules run in short batches, each batch being only allowed to run for a specific quantum of time—typically 1 second— producing a usable result in the end, and yielding control to the next module. To perform the whole computation, ProgressiVis loops over the modules as many times as necessary to converge to a result that the analyst considers satisfactory. This article introduces the new paradigm and its prototype implementation , provides some example, and lists some implications for future work.
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Dates and versions

hal-01202901 , version 1 (21-09-2015)


  • HAL Id : hal-01202901 , version 1


Jean-Daniel Fekete. ProgressiVis: a Toolkit for Steerable Progressive Analytics and Visualization. 1st Workshop on Data Systems for Interactive Analysis, Oct 2015, Chicago, United States. pp.5. ⟨hal-01202901⟩
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