Task-Oriented Guidance for Information Visualization Recommendation

Problem

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.

Aim

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:

• Ceneda, D., T. Gschwandtner, T. May, S. Miksch, H. - J. Schulz, M. Streit, and C. Tominski, Characterizing Guidance in Visual Analytics, IEEE Transactions on Visualization and Computer Graphics, 23(1):111-120, 2017.

• K., Wongsuphasawat, Z. Qu, D. Moritz, R. Chang, F. Ouk, A. Anand, J. Mackinlay, B. Howe, J. Heer,
  Voyager 2: Augmenting Visual Analysis with Partial View Specifications, forthcoming, CHI’17, May 06-11, 2017.

Collaboration:

• This project is performed in close cooperation with the Visual Analytics Group at the VRVis research center   (Harald Piringer, hp [at] vrvis.at)
• There are two ways to tackle and evaluate this problem

1. design and prototype implementation based on the Visual Analytics software platform "Visplore" (C++, OpenGL).

• A short description and a video about previous work on data quality assessment can be found at http://download.vrvis.at/va/papers/visplause/visplause.html
• German PR video of VRVis illustrating the Visplore software framework for industrial manufacturing data: http://download.vrvis.at/va/video/IndustrialQM.mp4

2. design and prototypical implementation in  D3 (Data-Driven Documents), Vega, or Vega-Lite, etc.

Previous knowledge:

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

Contact

• Silvia Miksch