Visualization

Abstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

 Visualization;  knowledge management;  analysis;  infovis;  vast;  knowledge-management;  

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Abstract: Investigative analysts who work with collections of text documents connect embedded threads of evidence in order to formulate hypotheses about plans and activities of potential interest. As the number of documents and the corresponding number of concepts and entities within the documents grow larger, sense-making processes become more and more difficult for the analysts. We have developed a visual analytic system called Jigsaw that represents documents and their entities visually in order to help analysts examine reports more efficiently and develop theories about potential actions more quickly. Jigsaw provides multiple coordinated views of document entities with a special emphasis on visually illustrating connections between entities across the different documents.

 Visualization;  knowledge management;  analysis;  infovis;  vast;  knowledge-management;  

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Abstract: Visualization tools are typically evaluated in controlled studies that observe the short-term usage of these tools by participants on preselected data sets and benchmark tasks. Though such studies provide useful suggestions, they miss the long-term usage of the tools. A longitudinal study of a bioinformatics data set analysis is reported here. The main focus of this work is to capture the entire analysts process that an analyst goes through from a raw data set to the insights sought from the data. The study provides interesting observations about the use of visual representations and interaction mechanisms provided by the tools, and also about the process of insight generation in general. This deepens our understanding of visual analytics, guides visualization developers in creating more effective visualization tools in terms of user requirements, and guides evaluators in designing future studies that are more representative of insights sought by users from their data sets

 Visual Analytics;  Visualization;  infovis;  

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Abstract: Sorry no abstract available for this article

 Visualization;  data visualisation;  decision support systems;  Knowledge Representation;  knowledge domain mapping, semantics;  

Abstract: Sorry no abstract available for this article

 Geography;  Visualization;  

Abstract: Sorry no abstract available for this article

 Geography;  Visualization;  

Abstract: This chapter reviews visualization techniques that can not only be utilized to map the evergrowing domain structure of scientific disciplines but that also support information retrieval and classification. In contrast to the comprehensive surveys done in a traditional way by Howard White and Katherine McCain (1997; 1998), the current survey not only reviews emerging techniques in interactive data analysis and information visualization, but also visualizes bibliographical structures of the field as an integral part of our methodology. The chapter starts with a review of the history of knowledge domain visualizations. We then introduce a general process flow for the visualization of knowledge domains and explain commonly used techniques. In the interest of visualizing the domain this article reviews, we introduce a bibliographic data set of considerable size, which includes articles from the citation analysis, bibliometrics, semantics, and visualization literatures. Using a tutorial style, we then apply various algorithms to demonstrate the visualization effects produced by different approaches and compare the different visualization results. At the same time, the domain visualizations reveal the relationships within and between the four fields that together form the topic of this chapter, domain visualization. We conclude with a discussion of promising new avenues of research and a general discussion.

 Visualization;  Knowledge Representation;  knowledge domain mapping, semantics;  Survey;  

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Abstract: In this chapter, we outline a project which traces its source of inspiration back to the grand visions of Vannevar Bush (scholarly trails of linked concepts), Doug Engelbart (highly interactive intellectual tools, particularly for argumentation), and Ted Nelson (large scale internet publishing with recognised intellectual property). In essence, we are tackling the age-old question of how to organise distributed, collective knowledge. Specifically, we pose the following question as a foil:In 2010, will scholarly knowledge still be published solely in prose, or can we imagine a complementary infrastructure that is ‘native’ to the emergingsemantic, collaborative web, enabling more effective dissemination and analysis of ideas?We are neither trying to replace textual narrative as an expressive medium, nor itsproducts such as books and peer reviewed publications. We seek instead to augment them by exploiting globally networked information in ways that – precisely because of its historical pedigree – the venerable prose publication cannot support. Conventional scholarly publications are the way they are through a co-evolution of notational form with print publishing technology, but are not designed in any way to take advantage of today’s information infrastructure. Still at a relatively early stage, our project is bringingto bear on this challenge a networked representational environment (a digital libraryserver based on an argumentation ontology (Buckingham Shum et al., 2000), semanticweb services (e.g. ontology-based reasoning, Li et al., 2002), and recent work on distributed collective practices (why and when individuals in a community of practice are willing to subscribe to a shared repository, and role of formalism, BuckinghamShum et al., 2002). All of these must be interacted with via a variety of user interfaces, of which a key component will be renderings of the network of argumentative claims—the focus of this chapter.

 Visualization;  Argumentation;  collaboration, ontology;  

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Abstract: The idea that mental representations are “small-scaled models of external reality” can be traced back to Craik (1943). This means, that a mental representation shares its structure with what it represents. Therefore, it is possible to model reality in thoughts and to learn about it without affecting it. It enables us to reason, to know what will or would be the case if this and that were to happen. Johnson-Laird (1983) developed a theory of mental models that can explain a wide variety of phenomena in reasoning. The power of mental models is grounded in the way the world is represented. It is not the logical structure (such as in propositions) or some artificial constructions (such as circles standing for sets) that are represented, but rather single objects taking part in a situation and the relations among them. Of course, this very basic idea must be extended, for sets must be representable as such, for example. However, the advantage of this account is that the world is represented in a simple and natural way. Johnson-Laird shows that reasoning with mental models leads to logically valid conclusions when no limit of capacity is assumed.

 Visualization;  perception;  cognition;  

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Abstract: One barrier to the uptake of geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualization and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, scatterplot, visual classifier, 2D map and image viewer, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, and supervised and unsupervised neural networks. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of different types of inference used in such knowledge creation activities is given, and related to the exploratory analysis tools described. By way of results, a detailed account of the use of these tools is presented, and various findings and insights generated are pointed out. The domain of application is the process of uncovering useful categories by which a taxonomy of landuse/landcover can be created. The proposed categories are then evaluated using a combination of neural and visual methods, to ensure their viability.

 knowledge discovery;  Visualization;  classification;  exploratory data analysis;  Geocomputation;  Inference;  

Abstract: Information visualization encounters a wide variety of different data domains. The visualization community has developed representation methods and interactive techniques. As a community, we have realized that the requirements in each domain are often dramatically different. In order to easily apply existing methods, researchers have developed a semiology of graphic representations. We have extended this research into a framework that includes operators and interactions in visualization systems, such as a visualization spreadsheet. We discuss properties of this framework and use it to characterize operations spanning a variety of different visualization techniques. The framework developed in this paper enables a new way of exploring and evaluating the design space of visualization operators, and helps end--users in their analysis tasks. Keywords: information visualization, operators, user interactions, view/value, framework, spreadsheet, design, extensibility, visualization systems. 1...

 Visualization Taxonomy;  Visualization Framework;  Visualization;  user interface;  

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Abstract: Scientists use a variety of visualization techniques to help understand computational fluid dynamics (CFD) datasets, but the interfaces to these techniques are generally two dimensional and therefore separated from the 3D view. Both rapid interactive exploration of datasets and precise control over the parameters and placement of visualization techniques are required to understand complex phenomena contained in these datasets. In this paper, we present work in progress on a 3D user interface for exploratory visualization of these datasets.

 Visualization;  Scientific Visualization;  

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Abstract: Sorry no abstract available for this article

 Data mining;  knowledge discovery;  Visualization;  data visualisation;  client-server systems;  decision support systems;  deductive databases;  knowledge acquisition;  relational databases;  MineSet;  computationally intensive tasks;  database visualization;  decision making;  relational data;  

Abstract: Interactive techniques are powerful tools for manipulating visualizations to analyze, communicate and acquire information. This is especially true for large data sets or complex 3D visualizations. Although many new types of interaction have been introduced recently, very little work has been done on understanding what their components are, how they are related and how they can be combined. This paper begins to address these issues with a framework for classifying interactive visualizations. Our goal is a framework that will enable us to develop toolkits for assembling visualization interfaces both interactively and automatically

 Visualization;  

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Abstract: Sorry no abstract available for this article

 Task Analysis;  Visualization;  

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