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.
Type de document :
Pré-publication, Document de travail
2016
Liste complète des métadonnées


https://hal.inria.fr/hal-01361430
Contributeur : Jean-Daniel Fekete <>
Soumis le : mercredi 7 septembre 2016 - 11:54:30
Dernière modification le : samedi 18 février 2017 - 01:14:35
Document(s) archivé(s) le : jeudi 8 décembre 2016 - 12:45:38

Fichier

progressivis.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

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

Collections

Citation

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

Partager

Métriques

Consultations de
la notice

175

Téléchargements du document

86