VAST contest dataset use in education

The IEEE Visual Analytics Science and Technology (VAST) Symposium has held a contest each year since its inception in 2006. These events are designed to provide visual analytics researchers and developers with analytic challenges similar to those encountered by professional information analysts. The VAST contest has had an extended life outside of the symposium, however, as materials are being used in universities and other educational settings, either to help teachers of visual analytics-related classes or for student projects. We describe how we develop VAST contest datasets that results in products that can be used in different settings and review some specific examples of the adoption of the VAST contest materials in the classroom. The examples are drawn from graduate and undergraduate courses at Virginia Tech and from the Visual Analytics “Summer Camp ” run by the National Visualization and Analytics Center in 2008. We finish with a brief discussion on evaluation metrics for education

Jigsaw meets Blue Iguanodon - The VAST 2007 Contest

This article describes our use of the Jigsaw system in working on the VAST 2007 Contest. Jigsaw provides multiple views of a document collection and the individual entities within those documents, with a particular focus on exposing connections between entities. We describe how we refined the identified entities in order to better facilitate Jigsaw’s use and how the different views helped us to uncover key parts of the underlying plot

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

Cognitive Foundations for Visual Analytics

In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions