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AVIZ Research team

Analysis and Visualization

Team presentation

Like many other fields, the sciences are being transformed by our rapidly-increasing abilities to collect, manage and understand vast amounts of data. A 2003 study estimated that the amount of data produced in the world was increasing by 50% each year. The amount of information made available through Internet search engines has grown exponentially for the last decade, and major Web search engines currently index more than 9 billion documents. However, since our brains and sensory capacities have not changed in the meantime, gaining competitive advantage from all this data depends increasingly on the effectiveness with which we support human abilities to perceive, understand, and act on it.
AVIZ is a multidisciplinary project-team that seeks to improve analysis and visualization of large, complex datasets by tightly integrating analysis methods with interactive visualization.

Research themes

  • Methods to visualize and smoothly navigate through large datasets;
  • Efficient analysis methods to reduce huge datasets to visualizable size;
  • Evaluation methods to assess their effectiveness and usability;
  • Engineering tools for building visual analytics systems that can access, search, visualize and analyze large datasets with smooth, interactive response.

AVIZ's approach is holistic: these themes are facets of building an analysis process optimized for discovery. All the methods and systems are designed to provide resources and minimize disruptions to the process of understanding data and forming insights.
The focus of AVIZ is the visual analysis of large networks (on the order of one million vertices and millions of edges) and time series (logs with billion of records captured continuously in real-time). The application domains include the analysis of large social networks (Wikipedia, Free Software Projects), biological networks, Business Intelligence, Digital Libraries and the time sequence of researchers' activities.

International and industrial relations

International collaborations with: University of Maryland (USA), University of Toronto (Canada),University of Calgary (Canada), University of Vienna (Austria), University of Sao-Paulo (Brazil), University of Delft (The Netherlands), University of Séoul (South Korean), and Microsoft Research Redmond (USA)
In France, we collaborate mainly with: université Paris-Sud, INRA, the National Archives, EDF and EHESS.

Keywords: Visualization Human-Computer Interaction Blockchain Uncertainty Open Data Big Data Data and Knowledge Analysis Online Analysis Big Data Analysis Large Graph Analysis Data Analysis Biology Neurosciences and cognitives sciences Data Science Humanities Psychology Economy and Finances