Task-Oriented Guidance for Information Visualization Recommendation


In many application domains, data involves a large number of attributes and categories. In industrial manufacturing, for example, numerous quality indicators are measured for each produced item along with process information such as the order ID, the used machinery, and much more. For such complex data, manually searching for visualizations that reveal interesting patterns such as correlations, trends, and outliers may become very tedious and time-consuming.


The goal of this work is to extend well-known views such as scatterplots, histograms, or categorical views by integrating recommendations on demand of view parameterizations which may be worth looking at. Typical examples could include “list all scatterplots showing correlations between data attributes for any data subset”, or “rank all time-series plots by the amount of showing a clear trend over the past weeks”. Important aspects of this work are thus to:

  1. identify meaningful tasks in the context of various visualization types
  2. implement corresponding quality metrics which should ideally be computed very efficiently in the background without disturbing the actual analysis
  3. design intuitive ways to present the possible visualization options as pre-views to the user in a way that is not obtrusive to the analysis and which scales to large number of possible variants (e.g., by clustering the variants to dissimilar groups).  

Other information

Starting point(s) for research:


  1. design and prototype implementation based on the Visual Analytics software platform "Visplore" (C++, OpenGL).
  1. design and prototypical implementation in  D3 (Data-Driven Documents), Vega, or Vega-Lite, etc.

Previous knowledge:

  • Information Visualization
  • C++, OpenGL, Java/JaveScript


Further information

Information Visualization, View Recommendation, Data Mining
Information Visualization (IV)
Not specified