A Programmer-Centric and Task-Optimized Object Graph Visualizer for Debuggers

Today, existing graph visualizers are not popular for debugging purposes because they are mostly visualization-oriented, rather than task-oriented, implementing general-purpose graph drawing algorithms. The latter explains why prominent integrated development environments still adopt traditional tree views. The authors introduce a debugging assistant with a visualization technique designed to better fit the actual task of defect detection in runtime object networks, while supporting advanced inspection and configuration features. Its design has been centered on the study of the actual programmer needs in the context of the debugging task, emphasizing: 1.) visualization style inspired by a social networking metaphor enabling easily identify who deploys objects (clients) and whom objects deploy (servers); 2.) inspection features to easily review object contents and associations and to search content patterns (currently regular expressions only); and 3.) interactively configurable levels of information detail, supporting off-line inspection and multiple concurrent views.

Articulate

This chapter presents an approach to enable non-visualization experts to craft advanced visualizations through the use of natural language as the primary interface. The main challenge in this research is in determining how to translate imprecise verbal queries into effective visualizations. To demonstrate the viability of the concept, the authors developed and evaluated a prototype, Articulate, which allows users to simply ask the computer for questions about their data and have it automatically generate visualizations that answer these questions. The authors discovered that by relieving the user of the burden of learning how to use a complex interface, they enable them to focus on articulating better scientific questions and wasting less time in producing unintended visualizations.

Visualization of Human Behavior Data

Although in recent years the Quantified Self (QS) application domain is growing, there are still some palpable fundamental problems that relegate the QS movement in a phase of low maturity. The first is a technological problem, and specifically, a lack of maturity in technologies for the collection, processing, and data visualization. This is accompanied by a perhaps more fundamental problem of deficit, bias, and lack of integration of aspects concerning the human side of the QS idea. The step that the authors tried to make in this chapter is to highlight aspects that could lead to a more robust approach in QS area. This was done, primarily, through a new approach in data visualization and, secondly, through a necessary management of complexity, both in technological terms and, for what concerns the human side of the whole issue, in theoretical terms. The authors have gone a little further stressing how the future directions of research could lead to significant impacts on both individual and social level.

Understanding Collections and Their Implicit Structures through Information Visualization

This chapter discusses how various approaches to information visualization can be used to assist users in understanding large digital collections and discovering relationships among the entities involved explicitly or implicitly in their development including people, organizations, and documents. Our main postulate is that visualization schemes, such as fisheye views, starfield displays, or self-organizing maps, when integrated and coupled with semantic layouts of topic areas, can significantly facilitate the analysis and discovery of existing and potential relationships among a wide range of entities. A series of developments illustrates how users play a key role in determining advantages and limitations of information visualization schemes, as well as in finding opportunities for improvement and new application areas.

Cognitive Processes and Traits Related to Graphic Comprehension

The subject of how visualizations and graphics in general can be understood by their viewers draws on theories from many fields of research. Such theories might address the formal structure of the visualization, the style and graphic design skills of the creator, the task driving the viewer’s interaction with the visualization, the type of data being represented, or the skills and experiences of viewer. This chapter focuses on this last question and presents a set of interrelated constructs and viewer traits that contribute to (or interfere with) a viewer’s ability to analyze a particular data visualization. The review covers spatial thinking skills, cognitive styles, mental models, and cognitive load in its discussion of theoretical constructs related to graphic comprehension. The review also addresses how these cognitive processes vary by age, sex, and disciplinary background–the most common demographic characteristics studied in relation to graphic comprehension. Together, the constructs and traits contribute to a diverse and nuanced understanding of the viewers of data visualizations.

Community Management Matters

Online communities provide technical support for organisations on a range of products and services. These communities are managed by dedicated online community managers who nurture and help the community grow. While visual analytics are increasingly used to support a range of data-intensive management processes, similar techniques have not been adopted into the community management field. Although relevant tools exist, the majority is developed in the lab, without conducting a domain analysis or eliciting user requirements, or is designed to support more general analytic tasks. In this chapter, the authors describe a case study in which we design, develop, and evaluate a visual analytics application with the help of Symantec’s online community management team. The authors suggest that the approach and the resulting application, called Petri, is an important step to promoting online community management as a strategic and data-driven process.

A Framework for Developing Diagram Applications

This chapter presents a framework for developing diagram applications. The diagrams refer to those graphs where nodes vary in shape and size used in real world applications, such as flowcharts, UML diagrams, and E-R diagrams. The framework is based on a model the authors developed for diagrams. The model is robust for diagrams and it can represent a wide variety of applications and support the development of powerful application-specific functions. The framework based on this model supports the development of automatic layout techniques for diagrams and the development of the linkage between the graph structure and applications. Automatic layout for diagrams is demonstrated and two case studies for diagram applications are presented.

Visualizing Information-Triage

In many ways, the promise of the Internet has been overshadowed by a sense of information overload and anxiety for many users. The production and publication of online material has become increasingly accessible and affordable, creating a confusing glut of information users must sift through to locate exactly what they want or need. Even a fundamental Google search can often prove paralyzing. In this chapter, the author examines the points at which design plays a role in the online search process, reconciles those points with the nature of sensemaking and the limitations of working memory, and suggests ways to support users with an information-triage system. The author then describes a speculative online searching prototype that explores these issues and the possibilities for information-triage.

From Data-Centered to Activity-Centered Geospatial Visualizations

As geospatial visualizations grow in popularity, their role in human activities is also evolving. While maps have been used to support higher-level cognitive activities such as decision-making, sense making, and knowledge discovery, traditionally their use in such activities has been partial. Nowadays they are being used at various stages of such activities. This trend is simultaneously being accompanied with another shift: a movement from the design and use of data-centered geospatial visualizations to activity-centered visualizations. Data-centered visualizations are primarily focused on representation of data from data layers; activity-centered visualizations, not only represent the data layers, but also focus on users’ needs and real-world activities—such as storytelling and comparing data layers with other information. Examples of this shift are being seen in some mashup techniques that deviate from standard data-driven visualization designs. Beyond the discussion of the needed shift, this chapter presents ideas for designing human-activity-centered geospatial visualizations.

Highlighting in Visual Data Analytics

Highlighting has been known as a basic viewing control mechanism in computer graphics and visualization for guiding users’ attention in reading diagrams, images, graphs, and digital texts. Due to the rapid development of theory and practice in information visualization and visual analytics, the role of ‘highlighting’ in computer graphics has been extended from just acting as a viewing control to being part of an interaction control and a visual recommendation mechanism that is important in modern information visualization and visual analytics. In this chapter, the authors present a brief literature review. They try to assign the word ‘highlighting’ a contemporary definition and attempt to give a formal summarization and classification of the existing and potential ‘highlighting’ methods that are to be applied in Information Visualization, Visual Analytics, and Knowledge Visualization. We also propose a new three-layer model of ‘highlighting’ and discuss the responsibilities of each layer accordingly.