How Progressive Visualizations Affect Exploratory Analysis

Emanuel Zgraggen, Alex Galakatos, Andrew Crotty, Jean Daniel Fekete, Tim Kraska

Research output: Contribution to journalArticlepeer-review

67 Scopus citations

Abstract

The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.

Original languageEnglish (US)
Pages (from-to)1977-1987
Number of pages11
JournalIEEE Transactions on Visualization and Computer Graphics
Volume23
Issue number8
DOIs
StatePublished - Aug 1 2017

Keywords

  • Exploratory analysis
  • insight-based evaluation
  • interactive visualization
  • progressive visualization
  • scalability

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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