Pages: pp. 1076-1077
Visual Analytics is an evolving field that, at its core, is directed to the science of analytical reasoning supported by highly interactive visual interfaces. People use visual analytics tools, methods, and techniques in all aspects of science, engineering, business, and government to synthesize data into information and knowledge, derive insight from massive, dynamic, and often conflicting data, detect the expected and discover the unexpected, provide timely, defensible, and understandable assessments, and communicate assessments effectively for action. Visual Analytics requires interdisciplinary research that integrates perspectives from information and scientific visualization with those from cognitive and perceptual sciences, statistics, mathematics, knowledge representation, management, and discovery technologies, decision sciences, and more.
Jim Thomas took a lead in defining the field of Visual Analytics and (with Kristin Cook) coedited a highly influential book in 2005, called Illuminating the Path, that presented results of a workshop to outline the scientific and practical challenges of the field. Within the community effort of the VisMaster Coordinated Action, funded by the European Union, this roadmap for Visual Analytics was updated in Solving Problems with Visual Analytics, coedited by Daniel Keim, Jörn Kohlhammer, Geoffrey Ellis, and Florian Mansmann in 2010.
The IEEE Conference on Visual Analytics Science and Technology (IEEE VAST), founded in 2006 as the IEEE Symposium on Visual Analytics Science and Technology, is the first international conference dedicated to advances in Visual Analytics Science and Technology. The scope of the conference, colocated with the annual IEEE Visualization Conference, the IEEE Information Visualization Conference, and other visualization events (jointly called VisWeek), includes both fundamental research contributions within Visual Analytics as well as applications of Visual Analytics, including applications in science, engineering, medicine, health, media, business, social interaction, and security and investigative analysis.
The IEEE Transactions on Visualization and Computer Graphics ( TVCG) has recognized and honored the importance of Visual Analytics from the beginning, and invites the authors of the best conference papers to submit substantively extended versions of VAST papers to the journal. For these papers, TVCG applies the usual standard in asking for more than 30 percent new material and insights compared to the conference paper. This special section presents the extended versions of the best papers of IEEE VAST 2011, which took place in October 2011 in Providence, Rhode Island. These papers were selected together with the best paper award selection committee, which was composed of three members who reviewed the top papers and their peer reviews. The three selected papers went through the regular and standard reviewing process of TVCG.
The papers presented here reflect the diversity of the growing field of visual analytics. Collectively, the set of papers exemplify three components that are central to visual analytics as a field.
The first paper is “Scalable Analysis of Movement Data for Extracting and Exploring Significant Places” by Gennady Andrienko, Natalia Andrienko, Christophe Hurter, Salvatore Rinzivillo, and Stefan Wrobel and was selected as the 2011 IEEE VAST best paper. The paper presents novel ways for analyzing large-scale spatio-temporal data resulting from tracking movement data. The authors present a visual analytics procedure consisting of four major steps: 1) event extraction from trajectories; 2) extraction of relevant places based on event clustering; 3) spatio-temporal aggregation of events or trajectories; and 4) analysis of the aggregated data. The benefits and limitations of their approach are illustrated by two real-world examples: first, exploring car movements in Milan (Italy), and second, analyzing the flight dynamics in France. Finally, they also demonstrate the scalability of their contribution.
The second paper is “The Longitudinal Use of SaNDVis: Visual Social Network Analytics in the Enterprise” by Adam Perer, Ido Guy, Erel Uziel, Inbal Ronen, and Michal Jacovi and was selected as an “honorable mentione paper.” Discovering relationships within an enterprise in the sense of finding other people and relevant network connections is a challenging task. This paper analyzes different approaches to mine, aggregate, and infer a social graph from social media inside an enterprise and proposes a visual analytics tool, called SaNDVis, that supports people-centric tasks, such as expertise location and team building inside an enterprise. One very interesting part of this paper is the longitudinal analysis of SaNDVis, which illustrate that effective integration of visual analytics tools into the user's ecosystem is a critical and challenging task.
The third paper is “How Visualization Layout Relates to Locus of Control and Other Personality Factors” by Caroline Ziemkiewicz, Alvitta Ottley, R. Jordan Crouser, Ashley Rye Yauilla, Sara L. Su, William Ribarsky, and Remco Chang and was also selected as an “honorable mentione paper.” This paper provides basic research on personality factors influencing interaction with visual analytics approaches. The authors isolate the factor “locus of control” (LOC) in relation to layout of visualizations, which was not done in previous studies. They conducted a user study with four visualizations that gradually shift from a list metaphor to a containment metaphor and compare the participants' speed, accuracy, and preference with their LOC and other personality factors. One of their main findings was that participants with an internal LOC perform more poorly with visualizations that employ a containment metaphor, while those with an external LOC perform well with such visualizations.
We would like to thank the authors for the effort that went into their submissions, the members of the best paper award selection committee, the program committee, and reviewers for their work in selecting and ordering contributions for the final program as well as for this special issue, and of course, the participants who made the IEEE Conference on Visual Analytics Science and Technology a great success.