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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https://hal.inria.fr/hal-01361430
Contributor : Jean-Daniel Fekete <>
Submitted on : Wednesday, September 7, 2016 - 11:54:30 AM
Last modification on : Tuesday, April 17, 2018 - 9:08:28 AM
Long-term archiving on : Thursday, December 8, 2016 - 12:45:38 PM

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  • HAL Id : hal-01361430, version 1
  • ARXIV : 1607.05162

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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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