Jian Chen

Ashwinkumar Ganesan, Kiante Brantley, Shimei Pan et al.

We present LDAExplore, a tool to visualize topic distributions in a given document corpus that are generated using Topic Modeling methods. Latent Dirichlet Allocation (LDA) is one of the basic methods that is predominantly used to generate topics. One of the problems with methods like LDA is that users who apply them may not understand the topics that are generated. Also, users may find it difficult to search correlated topics and correlated documents. LDAExplore, tries to alleviate these problems by visualizing topic and word distributions generated from the document corpus and allowing the user to interact with them. The system is designed for users, who have minimal knowledge of LDA or Topic Modelling methods. To evaluate our design, we run a pilot study which uses the abstracts of 322 Information Visualization papers, where every abstract is considered a document. The topics generated are then explored by users. The results show that users are able to find correlated documents and group them based on topics that are similar.

Haipeng Cai, Jian Chen, Alexander P. Auchus et al.

We present the design and prototype implementation of a scientific visualization language called Zifazah for composing 3D visualizations of diffusion tensor magnetic resonance imaging (DT-MRI or DTI) data. Unlike existing tools allowing flexible customization of data visualizations that are programmer-oriented, we focus on domain scientists as end users in order to enable them to freely compose visualizations of their scientific data set. We analyzed end-user descriptions extracted from interviews with neurologists and physicians conducting clinical practices using DTI about how they would build and use DTI visualizations to collect syntax and semantics for the language design, and have discovered the elements and structure of the proposed language. Zifazah makes use of the initial set of lexical terms and semantics to provide a declarative language in the spirit of intuitive syntax and usage. This work contributes three, among others, main design principles for scientific visualization language design as well as a practice of such language for DTI visualization with Zifazah. First, Zifazah incorporated visual symbolic mapping based on color, size and shape, which is a sub-set of Bertin's taxonomy migrated to scientific visualizations. Second, Zifazah is defined as a spatial language whereby lexical representation of spatial relationship for 3D object visualization and manipulations, which is characteristic of scientific data, can be programmed. Third, built on top of Bertin's semiology, flexible data encoding specifically for scientific visualizations is integrated in our language in order to allow end users to achieve optimal visual composition at their best. Along with sample scripts representative of our language design features, some new DTI visualizations as the running results created by end users using the novel visualization language have also been presented.