Abstract: This paper describes a model for expressing coordination in multiple view visualization systems. We present the model and describe a prototype implementation that illustrates the features of the model. Current visualization systems tend to have an informal and inconsistent approach to coordination. Our model takes a formal approach to describing widely used coordination concepts. The model is based on views sharing abstract objects such as the visualization parameters of the dataflow model. Additionaly, this paper describes how current coordinations in exploratory visualization work and how novel coordinations can be constructed using our model.

Abstract: The visual senses for humans have a unique status, offering a very broadband channel for information flow. Visual approaches to analysis and mining attempt to take advantage of our abilities to perceive pattern and structure in visual form and to make sense of, or interpret, what we see. Visual Data Mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this work, we try to investigate and expand the area of visual data mining by proposing new visual data mining techniques for the visualization of mining outcomes.

Abstract: Data visualization is one of the big issues of database research. OLAP as a decision support technology is highly related to the developments of data visualization area. In this paper we demonstrate how the Cube Presentation Model (CPM), a novel presentational model for OLAP screens, can be naturally mapped on the Table Lens, which is an advanced visualization technique from the Human-Computer Interaction area, particularly tailored for cross-tab reports. We consider how the user interacts with an OLAP screen and based on the particularities of Table Lens, we propose an automated proactive users support. Finally, we discuss the necessity and the applicability of advanced visualization techniques in the presence of recent technological developments.

Abstract: User studies offer a scientifically sound method to measure a visualization's performance. Reasons abound for pursuing user studies, particularly when evaluating the strengths and weaknesses of different visualization techniques. A good starting point in any study is the scientific or visual design question to be examined. This drives the process of experimental design. A poorly designed experiment will yield results of only limited value. Although a comprehensive discussion of experimental design is beyond the scope of the article, we offer suggestions and lessons learned. We also describe how we designed experiments to answer important questions from our own research.

Abstract: We developed an interactive visualization system that supports analysis and exploration of a large number of indicators that characterize the attractivity of cities in Switzerland. The application is available as a companion to a paper-based publication. This system is embedded in a conceptual framework of best practices in information visualization that we developed over the course of various past projects. It consists of several coordinated views that are tightly integrated, and that successfully reveal the data in its complexity. We present the components of this system, describe design decisions on how to integrate them into an application, and provide details on innovative refinements of various standard information visualization techniques.

Abstract: Existing visualization tools typically do not allow easy extension by new visualization techniques, and are often coupled with inflexible data input mechanisms. This paper presents EVolve, a flexible and extensible framework for visualizing program characteristics and behaviour. The framework is flexible in the sense that it can visualize many kinds of data, and it is extensible in the sense that it is quite straightforward to add new kinds of visualizations.The overall architecture of the framework consists of the core EVolve platform that communicates with data sources via a well defined data protocol and which communicates with visualization methods via a visualization protocol.Given a data source, an end-user can use EVolve as a stand-alone tool by interactively creating, configuring and modifying visualizations. A variety of visualizations are provided in the current EVolve library, with features that facilitate the comparison of multiple views on the same execution data. We demonstrate EVolve in the context of visualizing execution behaviour of Java programs.

Abstract: Must we continue to define a difference between information and scientific visualization? Scientific visualization evolved first in the late 1980’s while information visualization matured in the mid-1990’s. Scientific visualization is frequently considered to focus on the visual display of spatial data associated with scientific processes such as the bonding of molecules in computational chemistry. Information visualization examines developing visual metaphors for non-inherently spatial data such as the exploration of text-based document databases. This panel examines the effective, productive, and perhaps confusing tension between these subfields of visualization by highlighting the following issues:

Abstract: Is it necessary to continue to define a difference between information and scientific visualization? Is the determined need for these differences creating confusion rather than helping investigators understand how to effectively apply visual display techniques to data and information? The author considers how cartographic and geographic information techniques seem to span both scientific and information visualization. She discusses the future directions in bioinformatics visualization.

Abstract: An organizational visualization system is provided including obtaining organization information, obtaining overlay information, and processing the organization information and the overlay information to provide a visualization of the overlay information on an organization.

Abstract: We describe a model of the process by which people solve problems using information visualization systems. The model was based on video analysis of forty dyads who performed information visualization tasks in an experiment. We examined the following variables: focused questions vs. free data discovery, remote vs. collocated collaboration, and systems judged to have high and low transparency. The model describes the stages of reasoning and generating solutions with visual data. We found the model to be fairly robust across task type, collaborative setting, and system type, though subtle differences were found. We propose that system transparency can support some stages of the process, and that support is needed in the last stage to help users translate their findings from visual to written representations.

Abstract: The implementation of visual languages requires a wide range of conceptual and technical knowledge from issues of user interface design and graphical implementation to aspects of analysis and transformation for languages in general. We present a powerful toolset that incorporates such knowledge. Our toolset generates editors from high-level specifications. A language is specified by identifying certain patterns in the language structure and selecting a visual representation from a set of precoined solutions. Visual programs are represented by attributed abstract trees. Therefore, further phases of processing visual programs can be generated by state-of-the-art tools for language implementation. We demonstrate that even challenging visual languages can be implemented with reasonably little effort and with rather limited technical knowledge. The approach is suitable for a large variety of visual language styles.

Abstract: We present two techniques aiming at exploring databases through multivariate visualizations. Both techniques intend to deal with the problem caused by the limited amount of elements that can be presented simultaneously in traditional visual exploration procedures. The first technique, the Frequency Plot, combines data frequency with interactive filtering to identify clusters and trends in subsets of the database. Thus, graphical elements (lines, pixels, icons, or graphical marks) are color differentiated proportionally to how frequent the value being represented is, while interactive filtering allows the selection of interesting partitions of the database. The second technique, the Relevance Plot, corresponds to assigning different levels of color distinguishably to visual elements according to their relevance to a user's specified data properties set, which can be chosen visually and dynamically.

Abstract: Scientific Visualization is currently a very active and vital area of research, teaching and development. The success of Scientific Visualization is mainly due to the soundness of the basic premise behind it, that is, the basic idea of using computer-generated pictures to gain information and understanding from data (geometry) and relationships (topology). This is an extremely intuitive and very important concept which is having a profound and wide spread impact on the methodology of science and engineering.In this survey we are concentrating on three main research areas in Scientific Visualization• Intelligent Visualization Systems• Visualization of Vector- and Tensorfields• Augmented Reality Simulation

Abstract: Simulation and analysis of power systems often involves modeling extremely complex systems. This is particularly true when the model involves not just the electrical power system, but also other networks layered on top of the power system, such as the accompanying financial transactions. Such layered networks can often involve interactions that are not at all obvious. Interactive visualization can be a very effective means for determining otherwise hidden relationships between various elements in the network. This paper describes the application of interactive 3D for the visualization of electric power system operational and economic data. The paper first presents a 3D, one-line-based visualization and then provides several examples of how it could be used to display power system data.

Abstract: Introduction Visual design in general is interested in arranging information items (e.g., text, images, diagrams, pictures, tables) in such a way that it is visually attractive, perceptive and easily understandable. Visual design issues are raised in many domains of human activity such as user interface design, documentation development, presentation design, and graphic layout. This chapter heavily relies on techniques coming from visual design as used in typography to expand them to user interface design.

Abstract: We describe a system supporting the interactive exploration of threedimensional scientific data sets in a virtual reality (VR) environment. This system aids a scientist in understanding a data set by interactively placing and manipulating visualization primitives, e. g., isosurfaces or streamlines, and thereby finding features in the data and understanding its overall structure.

Abstract: Following a short survey of input data types on which to construct interactive visual user interfaces, we report on a new and recent implementation taking concept hierarchies as input data. The visual user interfaces express domain ontologies which are based on these concept hierarchies. We detail a web-based implementation, and show examples of usage. An appendix surveys related systems, many of them commercial.

Abstract: In this paper the visual reasoning that is part of visual thinking capabilities of the shape understanding system (SUS) is investigated. This research is a continuation of the authors' previous work focused on investigating understanding capabilities of the intelligent systems based on the shape understanding system. SUS is an example of the visual understanding system, where sensory information is transformed into the multilevel representation in the concept formation process that is part of the visual thinking capabilities. The visual reasoning involves transformation of the description of the object when passing consequent stages of the reasoning process and the reasoning and processing of the data are mutually dependent.

Abstract: Visualization exploration is the process of extracting insight from data via interaction with visual depictions of that data. Visualization exploration is more than presentation; the interaction with both the data and its depiction is as important as the data and depiction itself. Previous visualization research has focused on the generation of visualizations—the depiction—and not on the exploratory aspects of the visualization process. However, without user interfaces for and formal models of the visualization process, visualization exploration sessions cannot be fully utilized. Towards this end, this dissertation introduces a model and framework for the visualization exploration process. This research aims at providing a framework for capturing, representing, and manipulating information derived during the visualization discovery processes in a systematic manner. In particular, this work focuses on the exploration during the data analysis and visualization process through the use of intuitive graphical user interfaces. These interfaces provide a structured environment for the exploration of the visualization parameter space. The interfaces utilize a formal model of the visualization process that captures the fundamental operations performed during this exploration. The model is independent of the visualization performed or user interface utilized. In addition, instances of the model can be shared between users via an interoperable representation format. The goal of this work is to maximize user productivity by offering them an effective mechanism to fetch, edit, reuse, and share with others their visualizations which may include raw data, data associations, visualization results, and the steps taken to derive the visualization results.

Abstract: We conducted an empirical study to better understand colla-borative information visualization. We found that a system that offered fewer options for visualizations yielded more correct responses faster. Groups were more accurate but slower in solving problems than individuals. We identified different stages in visual discovery and found that collaboration benefits are from validating results and not from planning and system use. Tools to help translate and confirm the visualization would be of great benefit.

Abstract: This paper will present a new visual information retrieval system for metadata and the interaction techniques of the new system. The abundance of information we get while analyzing search results of an arbitrary query has to be channeled. This can be done by different visualizations and filter techniques. We use a Scatterplot in combination with a so called SuperTable to support the process of finding relevant information in an intuitive yet multifunctional way. Visual filters and the interaction between the visualiza- tions play an important role. By examples from a web and a movie database search fea- tures are demonstrated.

Abstract: Materials scientists use scientific visualization to explore very large multidimensional data sets. The Atomsviewer visualization system enables telepresence and provides multimodal views of simulation data.

Abstract: In scientific visualization, interaction tools allow users to navigate through volume and explore its features, helping information understanding. The use of sound as a data display tool (sonification) has also been shown to support information understanding and may help dealing with adding dimensions to a visual display. However, thus far the issues of data exploration tools and sonification have been treated separately by the visualization community. We present an interaction model for scientific visualization that considers the main issues related to sonification, and shows a prototype implemented to illustrate the proposed model. The prototype is a class library that implements a number of interaction tools with sonification functions, which is freely available and can be employed in general purpose visualization applications.

Abstract: Fusion is a web-based system that enables end-users to rapidly and dynamically construct personalized visualization workspaces without programming. Users first use advanced data schemas to link diverse data sources. Then they use visualization schemas to coordinate visualization components and data-mining algorithms according to the unique needs of their data and tasks. They create a custom interactive visualization workspace that can be published on the web. This is accomplished through the Fusion model and user interface that is based on schema concepts that are easy to learn and simple to use.

Abstract: ctive Visual System (IVS) exploits the vast cognition capabilities of humans and the onal power computers. The project grew rk by Dr. George Nagy and his graduate ie Zou, at Rensselaer Polytechnic Institute, ntly developed a system called CAVIAR r Assisted Visual Interactive System). n a desktop, CAVIAR identifies a flower features that are interactively extracted mage and submitted for comparison to a tabase. IVS has similar functionality to VIAR. However, in addition to a desktop, igned to run on a Sharp Zaurus SL-5500 computer, offering a greater degree of for practical and real-time use in the field. st neighbor algorithm provides a robust cognition technique using any number or on of five different features for ion. Our findings indicate that IVS s humans alone on speed and machines ccuracy.

Abstract: Users of information systems in time-critical domains are under constant pressure to digest and process information that is vital for their task. Thus, the user needs efficient information visualization that avoids displaying information that is not vital at the moment. Decision-centered visualization is an adaptive, interactive visualization system that supports decision making by integrating domain knowledge and knowledge about human decision making with an interactive visualization architecture. An important facet of this system is the representation of the underlaying domain knowledge. We will first describe the main modules and the main structures of the knowledge representation of the DCV system. We then describe how DCV uses the represented domain knowledge to guide the information visualization in time-critical applications.