Visual analytics for the big data era — A comparative review of state-of-the-art commercial systems

  • Authors:

  • Sebastian Mittelstadt;Michael Behrisch;Stefan Weber;Tobias Schreck;Andreas Stoffel;Rene Pompl;Daniel Keim;Holger Last;Leishi Zhang

  • Affiliations:

  • University of Konstanz, Germany;University of Konstanz, Germany;Siemens AG;University of Konstanz, Germany;University of Konstanz, Germany;Siemens AG;University of Konstanz, Germany;Siemens AG;University of Konstanz, Germany

  • Venue:
  • VAST '12 Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

Visual analytics (VA) system development started in academic research institutions where novel visualization techniques and open source toolkits were developed. Simultaneously, small software companies, sometimes spin-offs from academic research institutions, built solutions for specific application domains. In recent years we observed the following trend: some small VA companies grew exponentially; at the same time some big software vendors such as IBM and SAP started to acquire successful VA companies and integrated the acquired VA components into their existing frameworks. Generally the application domains of VA systems have broadened substantially. This phenomenon is driven by the generation of more and more data of high volume and complexity, which leads to an increasing demand for VA solutions from many application domains. In this paper we survey a selection of state-of-the-art commercial VA frameworks, complementary to an existing survey on open source VA tools. From the survey results we identify several improvement opportunities as future research directions.