Abstract: Data analysis often involves the comparison of complex objects. With the ever increasing amounts and complexity of data, the demand for systems to help with these comparisons is also growing. IncreasingLy, information visuaLization tools support such comparisons explicitLy, beyond simply aLLowing a viewer to examine each object individually. In this paper, we argue that the design of information visualizations of complex objects can, and should, be studied in general, that is independently of what those objects are. As a first step in developing this general understanding of comparison, we propose a general taxonomy of visual designs for comparison that groups designs into three basic categories, which can be combined. To clarify the taxonomy and validate its completeness, we provide a survey of work in information visualization related to comparison. Although we find a great diversity of systems and approaches, we see that all designs are assembled from the building blocks of juxtaposition, superposition and explicit encodings. This initial exploration shows the power of our model, and suggests future challenges in developing a generaL understanding of comparative visualization and faciLitating the development of more comparative visualization tools.

Abstract: The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important application areas. Effective visual analysis of graphs requires appropriate visual presentations in combination with respective user interaction facilities and algorithmic graph analysis methods. How to design appropriate graph analysis systems depends on many factors, including the type of graph describing the data, the analytical task at hand and the applicability of graph analysis methods. The most recent surveys of graph visualization and navigation techniques cover techniques that had been introduced until 2000 or concentrate only on graph layouts published until 2002. Recently, new techniques have been developed covering a broader range of graph types, such as timevarying graphs. Also, in accordance with ever growing amounts of graph-structured data becoming available, the inclusion of algorithmic graph analysis and interaction techniques becomes increasingly important. In this State-of-the-Art Report, we survey available techniques for the visual analysis of large graphs. Our review first considers graph visualization techniques according to the type of graphs supported. The visualization techniques form the basis for the presentation of interaction approaches suitable for visual graph exploration. As an important component of visual graph analysis, we discuss various graph algorithmic aspects useful for the different stages of the visual graph analysis process. We also present main open research challenges in this field.

Abstract: Narrative visualizations combine conventions of communicative and exploratory information visualization to convey an intended story. We demonstrate visualization rhetoric as an analytical framework for understanding how design techniques that prioritize particular interpretations in visualizations that "tell a story" can significantly affect end-user interpretation. We draw a parallel between narrative visualization interpretation and evidence from framing studies in political messaging, decision-making, and literary studies. Devices for understanding the rhetorical nature of narrative information visualizations are presented, informed by the rigorous application of concepts from critical theory, semiotics, journalism, and political theory. We draw attention to how design tactics represent additions or omissions of information at various levels-the data, visual representation, textual annotations, and interactivity-and how visualizations denote and connote phenomena with reference to unstated viewing conventions and codes. Classes of rhetorical techniques identified via a systematic analysis of recent narrative visualizations are presented, and characterized according to their rhetorical contribution to the visualization. We describe how designers and researchers can benefit from the potentially positive aspects of visualization rhetoric in designing engaging, layered narrative visualizations and how our framework can shed light on how a visualization design prioritizes specific interpretations. We identify areas where future inquiry into visualization rhetoric can improve understanding of visualization interpretation.

Abstract: This paper will present the general goal of and inspiration for our work on learning analytics, that relies on attention metadata for visualization and recommendation. Through information visualization techniques, we can provide a dashboard for learners and teachers, so that they no longer need to "drive blind". Moreover, recommendation can help to deal with the "paradox of choice" and turn abundance from a problem into an asset for learning.

Abstract: Despite typically receiving little emphasis in visualization research, interaction in visualization is the catalyst for the user's dialogue with the data, and, ultimately, the user's actual understanding and insight into these data. There are many possible reasons for this skewed balance between the visual and interactive aspects of a visualization. One reason is that interaction is an intangible concept that is difficult to design, quantify, and evaluate. Unlike for visual design, there are few examples that show visualization practitioners and researchers how to design the interaction for a new visualization in the best manner. In this article, we attempt to address this issue by collecting examples of visualizations with 'best-in-class' interaction and using them to extract practical design guidelines for future designers and researchers. We call this concept fluid interaction, and we propose an operational definition in terms of the direct manipulation and embodied interaction paradigms, the psychological concept of 'flow', and Norman's gulfs of execution and evaluation.

Abstract: Many well-cited theories for visualization design state that a visual representation should be optimized for quick and immediate interpretation by a user. Distracting elements like decorative "chartjunk" or extraneous information are avoided so as not to slow comprehension. Yet several recent studies in visualization research provide evidence that non-efficient visual elements may benefit comprehension and recall on the part of users. Similarly, findings from studies related to learning from visual displays in various subfields of psychology suggest that introducing cognitive difficulties to visualization interaction can improve a user's understanding of important information. In this paper, we synthesize empirical results from cross-disciplinary research on visual information representations, providing a counterpoint to efficiency-based design theory with guidelines that describe how visual difficulties can be introduced to benefit comprehension and recall. We identify conditions under which the application of visual difficulties is appropriate based on underlying factors in visualization interaction like active processing and engagement. We characterize effective graph design as a trade-off between efficiency and learning difficulties in order to provide Information Visualization (InfoVis) researchers and practitioners with a framework for organizing explorations of graphs for which comprehension and recall are crucial. We identify implications of this view for the design and evaluation of information visualizations.

Abstract: Our ability to visualize scientific data has evolved significantly over the last 40 years. However, this advancement does not necessarily alleviate many common pitfalls in visualization for scientific journals, which can inhibit the ability of readers to effectively understand the information presented. To address this issue within the context of visualizing environmental data, we list ten guidelines for effective data visualization in scientific publications. These guidelines support the primary objective of data visualization, i.e. to effectively convey information. We believe that this small set of guidelines based on a review of key visualization literature can help researchers improve the communication of their results using effective visualization. Enhancement of environmental data visualization will further improve research presentation and communication within and across disciplines.

Abstract: In visual analytics, sensemaking is facilitated through interactive visual exploration of data. Throughout this dynamic process, users combine their domain knowledge with the dataset to create insight. Therefore, visual analytic tools exist that aid sensemaking by providing various interaction techniques that focus on allowing users to change the visual representation through adjusting parameters of the underlying statistical model. However, we postulate that the process of sensemaking is not focused on a series of parameter adjustments, but instead, a series of perceived connections and patterns within the data. Thus, how can models for visual analytic tools be designed, so that users can express their reasoning on observations (the data), instead of directly on the model or tunable parameters? Observation level (and thus “observation”) in this paper refers to the data points within a visualization. In this paper, we explore two possible observation-level interactions, namely exploratory and expressive, within the context of three statistical methods, Probabilistic Principal Component Analysis (PPCA), Multidimensional Scaling (MDS), and Generative Topographic Mapping (GTM). We discuss the importance of these two types of observation level interactions, in terms of how they occur within the sensemaking process. Further, we present use cases for GTM, MDS, and PPCA, illustrating how observation level interaction can be incorporated into visual analytic tools.

Abstract: Visual analytics is an emerging research discipline aiming at making the best possible use of huge information loads in a wide variety of applications by appropriately combining the strengths of intelligent automatic data analysis with the visual perception and analysis capabilities of the human user. The major goal of visual analytics is the integration of these disciplines into visual analytics to acquire well-established and agreed upon concepts and theories, combining scientific breakthroughs in a single discipline to have a potential impact on visual analytics and vice versa. In a session at FET’11, the leaders of the thematic working groups of the recently finalised FET Open coordination action VisMaster CA presented the scientific challenges that were identified in the visual analytics research roadmap, and the connection between the various disciplines and the broader vision of visual analytics. This article contains excerpts from this research roadmap to motivate further research in this direction within FET.

Abstract: Studying how humans interact with abstract, visual representations of massive amounts of data provides knowledge about how cognition works in visual analytics. This knowledge provides guidelines for cognitive-aware design and evaluation of visual analytic tools. Different methods have been used to capture and conceptualize these processes including protocol analysis, experiments, cognitive task analysis, and field studies. In this article, we introduce Pair Analytics: a method for capturing reasoning processes in visual analytics. We claim that Pair Analytics offers two advantages with respect to other methods: (1) a more natural way of making explicit and capturing reasoning processes and (2) an approach to capture social and cognitive processes used to conduct collaborative analysis in real-life settings. We support and illustrate these claims with a pilot study of three phenomena in collaborative visual analytics: coordination of attention, cognitive workload, and navigation of analysis.

Abstract: Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques.

Abstract: A system and method for visually displaying and analyzing criminal and/or public health and safety data for geospatial and/or time variations, including the collection of incident data coupled with geographic and time data, filtering the symptom data based upon a selected time period and geographic range, and creating a visual result based upon statistical modeling including power transform and/or data normalization. According to at least one embodiment, the system for visually displaying and analyzing includes selecting and performing at least one aberration detection method and displaying the result to a user via a visual analytics arrangement.

Abstract: With today‘s ubiquity and popularity of social network applications, the ability to analyze and understand large networks in an efficient manner becomes critically important. However, as networks become larger and more complex, reasoning about social dynamics via simple statistics is not a feasible option. To overcome these limitations, we can rely on visual metaphors. Visualization nowadays is no longer a passive process that produces images from a set of numbers. Recent years have witnessed a convergence of social network analytics and visualization, coupled with interaction, that is changing the way analysts understand and characterize social networks. In this chapter, we discuss the main goal of visualization and how different metaphors are aimed towards elucidating different aspects of social networks, such as structure and semantics. We also describe a number of methods where analytics and visualization are interwoven towards providing a better comprehension of social structure and dynamics.

Abstract: Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. One key aspect that separates visual analytics from other related fields (InfoVis, SciVis, HCI) is the focus on analytical reasoning. While the final products generated at from an analytical process are of great value, research has shown that the processes of the analysis themselves are just as important if not more so. These processes not only contain information on individual insights discovered, but also how the users arrive at these insights. This area of research that focuses on understanding a user's reasoning process through the study of their interactions with a visualization is called Analytic Provenance, and has demonstrated great potential in becoming a foundation of the science of visual analytics. The goal of this workshop is to provide a forum for researchers and practitioners from academia, national labs, and industry to share methods for capturing, storing, and reusing user interactions and insights. We aim to develop a research agenda for how to better study analytic provenance and utilize the results in assisting users in solving real world problems.

Abstract: In modeling infectious diseases, scientists are studying the mechanisms by which diseases spread, predicting the future course of the outbreak, and evaluating strategies applied to control an epidemic. While recent work has focused on accurately modeling disease spread, less work has been performed in developing interactive decision support tools for analyzing the future course of the outbreak and evaluating potential disease mitigation strategies. The absence of such tools makes it difficult for researchers, analysts and public health officials to evaluate response measures within outbreak scenarios. As such, our research focuses on the development of an interactive decision support environment in which users can explore epidemic models and their impact. This environment provides a spatiotemporal view where users can interactively utilize mitigative response measures and observe the impact of their decision over time. Our system also provides users with a linked decision history visualization and navigation tool that support the simultaneous comparison of mortality and infection rates corresponding to different response measures at different points in time.

Abstract: Introduction Visual analytics aims at combining the strengths of human and electronic data processing. This is achieved by means of visualization and interactive visual interfaces, which allow humans and computers to converse and cooperate (Keim et al. 2008). Visual analytics is conceived as a multidisciplinary research field in which scientists specializing in information visualization, scientific visualization, and geographic visualization closely cooperate with researchers from analytical disciplines, such as statistical analysis and modeling, machine learning and data mining, and geographical analysis and modeling, on developing new approaches to solving complex problems faced by the modern society. Geovisual analytics (or geospatial visual analytics) deals with problems involving geographical space and various objects, events, phenomena, and processes populating it. Since most of the things populating space occur or change in time, geovisual analytics must give proper attention to time and relationships between space and time.

Abstract: Data visualization is an efficient and effective medium for communicating large amounts of information, but the design process can often seem like an unexplainable creative endeavor. This concise book aims to demystify the design process by showing you how to use a linear decision-making process to encode your information visually. Delve into different kinds of visualization, including infographics and visual art, and explore the influences at work in each one. Then learn how to apply these concepts to your design process.Learn data visualization classifications, including explanatory, exploratory, and hybrid Discover how three fundamental influencesthe designer, the reader, and the datashape what you create Learn how to describe the specific goal of your visualization and identify the supporting data Decide the spatial position of your visual entities with axes Encode the various dimensions of your data with appropriate visual properties, such as shape and color See visualization best practices and suggestions for encoding various specific data types

Abstract: Image analysis algorithms are often highly parameterized and much human input is needed to optimize parameter settings. This incurs a time cost of up to several days. We analyze and characterize the conventional parameter optimization process for image analysis and formulate user requirements. With this as input, we propose a change in paradigm by optimizing parameters based on parameter sampling and interactive visual exploration. To save time and reduce memory load, users are only involved in the first step - initialization of sampling - and the last step - visual analysis of output. This helps users to more thoroughly explore the parameter space and produce higher quality results. We describe a custom sampling plug-in we developed for CellProfiler - a popular biomedical image analysis framework. Our main focus is the development of an interactive visualization technique that enables users to analyze the relationships between sampled input parameters and corresponding output. We implemented this in a prototype called Paramorama. It provides users with a visual overview of parameters and their sampled values. User-defined areas of interest are presented in a structured way that includes image-based output and a novel layout algorithm. To find optimal parameter settings, users can tag high- and low-quality results to refine their search. We include two case studies to illustrate the utility of this approach.

Abstract: The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization- a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown - right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.

Abstract: Co-authorship networks are examples of social networks, in which researchers are linked by their joint publications. Like many other instances of social networks, co-authorship networks contain rich sets of valuable data. In this paper, we propose a visual analytic tool, called SocialVis, to analyze and visualize these networks. In particular, SocialVis first applies frequent pattern mining to discover implicit, previously unknown and potential useful social information such as teams of multiple frequently collaborating researchers, their composition, and their collaboration frequency. SocialVis then uses a visual representation to present the mined social information so as to help users get a better understanding of the networks.

Abstract: Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

Abstract: The visualization and analysis of dynamic networks have become increasingly important in several fields, for instance sociology or economics. The dynamic and multi-relational nature of this data poses the challenge of understanding both its topological structure and how it changes over time. In this paper we propose a visual analytics approach for analyzing dynamic networks that integrates: a dynamic layout with user-controlled trade-off between stability and consistency; three temporal views based on different combinations of node-link diagrams (layer superimposition, layer juxtaposition, and two-and-a-half-dimensional view); the visualization of social network analysis metrics; and specific interaction techniques for tracking node trajectories and node connectivity over time. This integration of visual, interactive, and automatic methods supports the multi-faceted analysis of dynamically changing networks.

Abstract: Software analytics is to enable software practitioners to perform data exploration and analysis in order to obtain insightful and actionable information for data-driven tasks around software and services. In this position paper, we advocate that when applying analytic technologies in practice of software analytics, one should (1) incorporate a broad spectrum of domain knowledge and expertise, e.g., management, machine learning, large-scale data processing and computing, and information visualization; and (2) investigate how practitioners take actions on the produced information, and provide effective support for such information-based action taking. Our position is based on our experiences of successful technology transfer on software analytics at Microsoft Research Asia.

Abstract: This paper presents a new approach to detecting and tracking changes in word meaning by visually modeling and representing diachronic development in word contexts. Previous studies have shown that computational models are capable of clustering and disambiguating senses, a more recent trend investigates whether changes in word meaning can be tracked by automatic methods. The aim of our study is to offer a new instrument for investigating the diachronic development of word senses in a way that allows for a better understanding of the nature of semantic change in general. For this purpose we combine techniques from the field of Visual Analytics with unsupervised methods from Natural Language Processing, allowing for an interactive visual exploration of semantic change.

Abstract: Information visualization appears to be a promising technique for improving the business practices in today's electric power industry. The legacy power system visualization tools, however, restrict the visualization process to follow a limited number of pre-defined patterns created by human designers, thus hindering users' ability to discover. This paper proposes a data-driven approach to interactive visualization of power systems. The proposed approach relies on developing powerful data manipulation algorithms to create visualizations based on the characteristics of empirically or mathematically derived data. Based on this approach, a data-driven model exploratory tool has been developed to enable users to visualize the power system's physical/electrical configurations at various levels and from different perspectives. The conducted case studies have demonstrated that the data-driven approach could result in an interactive and user-driven power system visualization tool that fosters scientific understanding and insight, therefore unleashing the power of visualization.

Abstract: We investigate the decision process as applied to the practical task of choosing a financial portfolio. We developed PortfolioCompare, an interactive visual analytic decision support tool that helps the consumer quickly create, compare and choose among several portfolios consisting of different financial instruments. PortfolioCompare facilitates the analysis of risk and return aspects of each portfolio considered. We investigate behavior in this task using an economic experiment in which the user actively creates and compares portfolios from a set of funds. We elicit risk preferences using a separate task and find that subjects using PortfolioCompare make decisions that are closer to their risk tolerance as compared to subjects presented with similar information in textual form. This finding suggests that PortfolioCompare helps understand risk aspects of portfolios. Portfolio selections are improved during the course of the decision process, suggesting that this tool is valuable for decision support.

Abstract: We introduce WordBridge, a novel graph-based visualization technique for showing relationships between entities in text corpora. The technique is a node-link visualization where both nodes and links are tag clouds. Using these tag clouds, WordBridge can reveal relationships by representing not only entities and their connections, but also the nature of their relationship using representative keywords for nodes and edges. In this paper, we apply the technique to an interactive web-based visual analytics environment---Apropos---where a user can explore a text corpus using WordBridge. We validate the technique using several case studies based on document collections such as intelligence reports, co-authorship networks, and works of fiction.

Abstract: We present Cardiogram, a visual analytics system that supports automotive engineers in debugging masses of traces each consisting of millions of recorded messages from in-car communication networks. With their increasing complexity, ensuring these safety-critical networks to be error-free has become a major task and challenge for automotive engineers. To overcome shortcomings of current analysis tools, Cardiogram combines visualization techniques with a data preprocessing approach to automatically reduce complexity based on engineers' domain knowledge. In this paper, we provide the findings from an exploratory, three-year field study within a large automotive company, studying current practices of engineers, the challenges they meet and the characteristics for integrating novel visual analytics tools into their work practices. We then introduce Cardiogram, discuss how our field analysis influenced our design decisions, and present a qualitative, long-term, in-depth evaluation. Results of this study showed that our participants successfully used Cardiogram to increase the amount of analyzable information, to externalize domain knowledge, and to provide new insights into trace data. Our design approach finally led to the adoption of Cardiogram into engineers' daily practices.