Using the VAST Challenge in Undergraduate CS Research

The Visual Analytics Science and Technology (VAST) Challenge is a yearly competition designed to push forward visual analytics research through synthetic, yet realistic analytic tasks. In this paper, we discuss the challenges and the successes we have experienced incorporating the VAST Challenge and associated datasets into undergraduate research programs at two liberal arts colleges. We advocate for increased undergraduate participation in this and similar competitions, arguing they afford unique opportunities for positive development in early researchers

The state of the art in integrating machine learning into visual analytics

Visual analytics systems combine machine learning or other analytic techniques with interactive data visualization to promote sensemaking and analytical reasoning. It is through such techniques that people can make sense of large, complex data. While progress has been made, the tactful combination of machine learning and data visualization is still under-explored. This state-of-the-art report presents a summary of the progress that has been made by highlighting and synthesizing select research advances. Further, it presents opportunities and challenges to enhance the synergy between machine learning and visual analytics for impactful future research directions

Interactive visualization systems and data integration methods for supporting discovery in collections of scientific information

Technological developments have been enabling additional sharing and reuse of scientific information. Current indexing methods support query-based search and filtering, however they do not support overviews and exploration. Due to these limitations of existing indexing methods, it is challenging to discover records and connections that relate information in new and potentially insightful ways. We developed prototype systems and computational methods for integrating collections from multiple sources within a domain into a single, unified graph data structure. Graph-theoretic measures and visualizations were then applied to identify relations and records that support discovery tasks. Three collections of molecular information were studied: (1) influenza protein sequences from the National Center for Biotechnology Information, (2) Open Notebook Science notebooks and databases from Drexel University and other academic chemical research laboratories, and (3) project data from drug discovery projects at Pfizer R&D. We designed methods for data integration within these collections. We then analyzed the integrated collections to design interactive visual tools and computational methods that could systematically identify relations and records that have a high potential to lead to novel discoveries in these areas. We conducted interviews with domain experts to evaluate the effectiveness of these designs. These studies demonstrate the feasibility of the new indexing methods to improve the discoverability of novel connections across multiple collections within a domain.Ph.D., Information Science -- Drexel University, 201

The Beneficial Role of the VAST Challenges in the Evolution of GeoTime and nSpace2

Oculus participated in the first VAST Challenge in 2006 and in every subsequent year except one. Although we have won 5 awards in the last 7 years, they are not the main reason we spend much time and effort preparing our submission each year. The VAST Challenge has proven to be a valuable exercise in evaluating and refining our tools and our thinking. No organization that has the desire to create the best possible analytic tools should pass by the unique opportunity to work on a realistic set of tasks, against nontoy datasets containing ground truth, and to be evaluated by both senior analysts and experts within the visual analytics community. Oculus staff and products have benefited from the experience as have the whole visual analytics community researchers and practitioners