Nascent Papers

Trevor

Tentative Title(s):

Extracting Semantic Content from Interaction Histories in 3D, Time-varying Visualizations

Interaction Histories for Collaboration, Search, and Prediction in 3D, Time-Varying Visualizations

Contributions:

1. Introduces a generalizable framework for automatically generating sharable, editable, searchable interaction histories in time-varying 3D applications.
2. Demonstrates utility of Relational Markov Models (RMMs) in extracting semantic information from interaction histories, useful for prediction and automation in scientific exploration.
3. Contributes the technical implementation details (software itself? open source project?) for applying said methods in pre-existing applications.

Results:

1. Techniques were applied in 3 existing applications: Animal kinematics from CT & X-ray, bat flight kinematics from light capture, and (__?? brain stuff, wrist stuff??, maybe infovis stuff like proteomics??___)
2. User evaluation of history generation matched user-defined histories in X% of cases. (Fully-automated, semi-automated, manual)
3. Collect data on collaboration? Anecdotal evidence on how tools were used for collaboration? (Need to get on this quickly, with new data sets that are actively being explored. Talk to Beth, Sharon.)
4. User study on task completion times with tools versus without tools.
5. Relational models evaluated against survey data. i.e. User was trying to uncover this in series of interactions, system interpreted interactions as this or that.

(Need to think more about how to objectify the previous two bullets.)

Abstract: TBD.

References:

Why interaction is more powerful than algorithms

Relational Markov Models and their Application to Adaptive Web Navigation

Visualizing Interaction History on a Collaborative Web Server

Distributed Cognition: Toward a New Foundation for Human-Computer Interaction Research

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Çağatay

Coloring 3d line fields using Boy’s real projective plane immersion

Abstract:

It’s often useful to visualize a line field, a function that sends each point P of the plane or of space to a line through P; such fields arise in the study of tensor fields, where the principal eigendirection at each point determines a line (but not a vector, since if v is an eigenvector, so is −v). To visualize such a field, we often assign a color to each line; thus we consider the coloring of line fields as a mapping from the real projective plane (RP2) to color space. Ideally, such a coloring scheme should be smooth and one-to-one, so that the color uniquely identifies the line; unfortunately, there is not such mapping. We introduce Boy’s surface, an immersion of the projective plane in 3D, as a model for coloring line fields, and show results from its application in visualizing orientation in diffusion tensor fields. This coloring method is smooth and one to one except on a set of measure zero (the double curve of Boy’s surface).

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