Topic Similarity Networks: Visual Analytics for Large Document Sets

We investigate ways in which to improve the interpretability of LDA topic models by better analyzing and visualizing their outputs. We focus on examining what we refer to as topic similarity networks: graphs in which nodes represent latent topics in text collections and links represent similarity among topics. We describe efficient and effective approaches to both building and labeling such networks. Visualizations of topic models based on these networks are shown to be a powerful means of exploring, characterizing, and summarizing large collections of unstructured text documents. They help to "tease out" non-obvious connections among different sets of documents and provide insights into how topics form larger themes. We demonstrate the efficacy and practicality of these approaches through two case studies: 1) NSF grants for basic research spanning a 14 year period and 2) the entire English portion of Wikipedia.Comment: 9 pages; 2014 IEEE International Conference on Big Data (IEEE BigData 2014

Data Visualization in Games

Charts and graphs are becoming ubiquitous in daily life, regularly seen as people explore stocks, politics, social media, and more. Data visualization literacy is the measure of ones ability to interpret visually-displayed data. In this project, we create a visualization-literacy-focused game as an engaging and entertaining method of testing and improving data visualization literacy. We evaluate our project through an experiment with pre- and post- tests based on peer-reviewed visualization literacy surveys. The results of these evaluations suggest that our game is capable of improving the data visualization literacy of a player. This success opens a path to future game-based methods of improving data visualization literacy

Obvious: a meta-toolkit to encapsulate information visualization toolkits. One toolkit to bind them all

This article describes “Obvious”: a meta-toolkit that abstracts and encapsulates information visualization toolkits implemented in the Java language. It intends to unify their use and postpone the choice of which concrete toolkit(s) to use later-on in the development of visual analytics applications. We also report on the lessons we have learned when wrapping popular toolkits with Obvious, namely Prefuse, the InfoVis Toolkit, partly Improvise, JUNG and other data management libraries. We show several examples on the uses of Obvious, how the different toolkits can be combined, for instance sharing their data models. We also show how Weka and RapidMiner, two popular machine-learning toolkits, have been wrapped with Obvious and can be used directly with all the other wrapped toolkits. We expect Obvious to start a co-evolution process: Obvious is meant to evolve when more components of Information Visualization systems will become consensual. It is also designed to help information visualization systems adhere to the best practices to provide a higher level of interoperability and leverage the domain of visual analytics

How can 3D Game Engines create Photo-Realistic Interactive Architectural Visualizations?

Architectural Visualizations are the evolution from a once used technique of Architectural Rendering. Through the proliferation of modern technology, the industry has progressed by using more contemporary applications to produce three dimensional (3D) renders for the output of images and videos. Using such applications allows for “photo-realistic” visuals that have an uncanny representation to a real-life environment, for clients to visualize proposed buildings, that can offer both interior and exterior environments. However, such applications lack a key component that could extend the platform that the visualization industry currently uses. Through recent technological developments pertaining to game engines, virtual game environments can render high fidelity visuals in real-time whilst providing interactive elements for deployment to various devices. This paper aims to create and implement an alternative method to the conventional three-dimensional pre-rendered visualizations, using a 3D game engine that can provide an interactive based solution, distributed to a computer device, for both the industry and the end user to experienc

USABILITY TESTING OF THE M.A.E.G.U.S. SERIOUS GAME

Interpreting raw data in serious games and simulations can be a time consuming and uninteresting task without visualizations. This study proposes one possible solution for an interface that incorporates data visualizations for Whittinghill and Nataraja\u27s (2013) MAEGUS simulation, a serious game used to increase the retention of wind energy and solar energy concepts in students, while still being fun. After the interface was designed and developed, a think aloud usability test was conducted to answer the following research questions: how do students use a series of information visualizations to operate a multi-variate game-based simulation and what are some the usability issues the students face in the simulation? A thematic analysis was then conducted to document and organize the responses

iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the fndings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users' gaze, helping casual fans appreciate basketball games without being overwhelmed. We confrmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.Comment: ACM CHI2