Week 8 – Project Update & Challenges (4)

(4) What are your challenges for Data Visualizations? 

What kinds of “things” can we see in a visualization? That’s the art of visualization design! We’re trying to plot the data such that the features we are interested in are obviously visible. The key principle is, don’t try to show everything at once. “Overview first, zoom and filter, then details-on-demand.” – UI Design scientists Ben Shneiderman. So we use interactive designs to allow users’ the ability and freedom to explore more.
1. “Data” – Methods have to deal with modern data formats and data sets: How can the technologies be adapted to deal with streaming and probably non i.i.d. data sets? How can specific data formats be visualized appropriately such as spatio-temporal data, spectral data, data characterized by a general probably non-metric dissimilarity measure, etc.? How can we deal with heterogeneous data and different credibility? How can the dissimilarity measure be adapted to emphasize the aspects which are relevant for visualization?

2. “Algorithms” Available techniques for specific tasks should be combined in a canonic way: How can unsupervised learning techniques be combined to construct good visualizations? For instance, how can we effectively combine techniques for clustering, collaborative filtering, and topic modeling with dimensionality reduction to construct scatter plots that reveal the similarity between groups of data, movies, or documents? How can we arrive at context dependent visualization?

3. “Users” – Visualization techniques should be ready to use for users outside the field: Which methods are suited to users outside the field? How can the necessity be avoided to set specific technical parameters by hand or choose from different possible mathematical algorithms by hand? Can this necessity be substituted by intuitive interactive mechanisms which can be used by non-experts?

4. “Theories”- Visualization techniques should be accompanied by theoretical guarantees: What are reasonable mathematical specifications of data visualization to shape this inherently ill-posed problem? Can this be controlled by the user in an efficient way? How can visualization be evaluated? What are reasonable benchmarks? What are reasonable evaluation measures?
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