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Preprints, Working Papers, ... Year : 2016

Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Abstract

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.
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Dates and versions

hal-01361430 , version 1 (07-09-2016)

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Jean-Daniel Fekete, Romain Primet. Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis. 2016. ⟨hal-01361430⟩
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