Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

A hash tag is defined to be a word or phrase prefixed with the symbol #. It is widely used in current social media sites including Twitter and Google+, and serves as a significant meta tag to categorize users' messages, to propagate ideas and topic trends. The use of hash tags has become an integral part of the social media culture. However, the free-form nature and the varied contexts of hash tags bring challenges: how to understand hash tags and discover their relationships? In this paper, we propose Tag-Latent Dirichlet Allocation (TLDA), a new topic modeling approach to bridge hash tags and topics. TLDA extends Latent Dirichlet Allocation by incorporating the observed hash tags in the generative process. In TLDA, a hash tag is mapped into the form of a mixture of shared topics. This representation further enables the analysis of the relationships between the hash tags. Applying our model to tweet data, we first illustrate the ability of our approach to explain hard-to-understand hash tags with topics. We also demonstrate that our approach enables users to further analyze the relationships between the hash tags.

Analyzing large textual collections has become increasingly challenging given the size of the data available and the rate that more data is being generated. Topic-based text summarization methods coupled with interactive visualizations have presented promising approaches to address the challenge of analyzing large text corpora. As the text corpora and vocabulary grow larger, more topics need to be generated in order to capture the meaningful latent themes and nuances in the corpora. However, it is difficult for most of current topic-based visualizations to represent large number of topics without being cluttered or illegible. To facilitate the representation and navigation of a large number of topics, we propose a visual analytics system--HierarchicalTopic (HT). HT integrates a computational algorithm, Topic Rose Tree, with an interactive visual interface. The Topic Rose Tree constructs a topic hierarchy based on a list of topics. The interactive visual interface is designed to present the topic content as well as temporal evolution of topics in a hierarchical fashion. User interactions are provided for users to make changes to the topic hierarchy based on their mental model of the topic space. To qualitatively evaluate HT, we present a case study that showcases how HierarchicalTopics aid expert users in making sense of a large number of topics and discovering interesting patterns of topic groups. We have also conducted a user study to quantitatively evaluate the effect of hierarchical topic structure. The study results reveal that the HT leads to faster identification of large number of relevant topics. We have also solicited user feedback during the experiments and incorporated some suggestions into the current version of HierarchicalTopics.

Displays supporting stereoscopy and head-coupled motion parallax can enhance human perception of complex 3D datasets. This has been studied extensively for datasets containing 3D surfaces and 3D networks but less for so volumetric data. Volumetric data is characterized by a heavy presence of transparency, occlusion and highly ambiguous spatial structure. There are many different rendering and visualization algorithms and interactive techniques that enhance perception of volume data and these techniques' effectiveness have been evaluated. However, the effect of VR displays on perception of volume data is less well studied. Therefore, we conduct two experiments on how various display conditions affect a participant's depth perception accuracy of a volumetric dataset. A demographic pre-questionnaire also allows us to separate the accuracy differences between participants with more and less experience with 3D games and VR. Our results show an overall benefit for stereo with head-tracking for enhancing perception of depth in volumetric data. Our study also suggests that familiarity with 3D games and VR type technology affects the users'ability to perceive such data and affects the accuracy boost due to VR displays.

Intuition is an important yet ill-defined factor when designing effective multi-touch interactions. Throughout the research community, there is a lack of consensus regarding both the nature of intuition and, more importantly, how to systematically incorporate it into the design of multi-touch gestural interactions. To strengthen our understanding of intuition, we surveyed various domains to determine the level of consensus among researchers, commercial developers, and the general public regarding which multi-touch gestures are intuitive, and which of these gestures intuitively lead to which interaction outcomes. We reviewed more than one hundred papers regarding multi-touch interaction, approximately thirty of which contained key findings we report herein. Based on these findings, we have constructed a framework of five factors that determine the intuition of multi-touch interactions, including direct manipulation, physics, feedback, previous knowledge, and physical motion. We further provide both design recommendations for multi-touch developers and an evaluation of research problems which remain due to the limitations of present research regarding these factors. We expect our survey and discussion of intuition will raise awareness of its importance, and lead to the active pursuit of intuitive multi-touch interaction design.

Displays supporting stereopsis and head location based motion parallax can enhance human perception of complex three dimensional datasets. This has been demonstrated for datasets containing 3D surfaces and 3D networks. Yet many domains, such as medical imaging, weather and environment simulations and fluid flow, generate complex volumetric data. This poster present results of an initial formal experiment that examines the effectiveness of various display conditions on depth perception of volumetric data. There is an overall benefit for stereoscopy with head-tracking in enhancing depth perception. Further, familiarity with 3D games and VR-like hardware improves the users'ability to perceive such data.

Abstract We present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high‐performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I‐SI, closely integrates data‐driven analytical methods and user‐centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I‐SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I‐SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I‐SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.

We present research focused on designing visual analytics (VA) systems for workers in organizational environments. We focus on business analysts and asset managers, who work collaboratively to analyze information and make decisions. Through extensive investigations in two organizational environments, we found that these users struggle with managing and analyzing information from multiple perspectives. Their current tools lack support for aggregating, organizing, and sharing such information. To address their needs, we characterized their analytic workflows, extracted specific key knowledge actions for each task commonly found in these workflows, and designed and evaluated two visual analytics systems that support and encapsulate these knowledge actions. We provide design guidelines that should be used when designing visual analytics systems, and illustrate their effectiveness with two systems built by following them.

In an enterprise environment, business information, such as project proposals and product discussions, is dynamic and often embedded in documents and document activities (e.g, emails, Web pages and office documents). Because this information is essential to business processes, corporate employees need an effective means to retrieve it. Some commercial products, including Google Desktop, provide keyword searches for finding some of this information. However, this approach is not always effective as successful keyword searches can be difficult to construct, and even the best queries may fail to find some important materials. In this paper, we present Taste (Temporal Activities and Story TElling), an interactive visual analytics system that enhances the enterprise employee's capabilities in searching and sharing diverse and dynamic business information. Taste was designed, after interviews with corporate employees, to follow their information retrieval cues and help them manage, review and share the business information embedded in their document activities. Results of our lab and field studies validate that Taste provides employees the confidence and necessary features to more efficiently and effectively retrieve business information from their documents and activities.

Abstract Bridges deteriorate over their life cycles and require continuous maintenance to ensure their structural integrity, and in turn, the safety of the public. Maintaining bridges is a multi‐faceted operation that requires both domain knowledge and analytics techniques over large data sources. Although most existing bridge management systems (BMS) are very efficient at data storage, they are not as effective at providing analytical capabilities or as flexible at supporting different inspection technologies. In this paper, we present a visual analytics system that extends the capability of current BMSs. Based on a nation‐wide survey and our interviews with bridge managers, we designed our system to be customizable so that it can provide interactive exploration, information correlation, and domain‐oriented data analysis. When tested by bridge managers of the U.S. Department of Transportation, we validated that our system provides bridge managers with the necessary features for performing in‐depth analysis of bridges from a variety of perspectives that are in accordance to their typical workflow.

Abstract Recent increases in terrorist activity around the world have made analyzing and understanding such activities more critical than ever. With the help of organizations such as the National Center for the Study of Terrorism and Responses to Terrorism (START), we now have detailed historical information on each terrorist event around the world since 1970. However, due to the size and complexity of the data, identifying terrorists' patterns and trends has been difficult. To better enable investigators in understanding terrorist activities, we propose a visual analytical system that focuses on depicting one of the most fundamental concepts in investigative analysis, the five W's (who, what, where, when, and why). Views in our system are highly correlated, and each represents one of the W's. With this approach, an investigator can interactively explore terrorist activities efficiently and discover reasons of attacks (why) by identifying patterns temporally (when), geo‐spatially (where), between multiple terrorist groups (who), and across different methods or modes of attacks (what). By coupling a global perspective with the details gleaned from asking these five questions, the system allows analysts to think both tactically and strategically.