CiteULike: Group: mobilecomputing - library [52 articles]

Interactive data visualization using focusing and linking

Andreas Buja — 2006-06-07T20:09:45-00:00

(1991), pp. 156-163.

Data visualization: the end of the rainbow

BE Rogowitz — 2006-06-07T19:50:52-00:00

Spectrum, IEEE, Vol. 35, No. 12. (1998), pp. 52-59.

A rainbow colormap is often supplied as a default in visualization software. In this kind of colormap, red is mapped to the highest data value, blue to the lowest, and the other data values are interpolated along the full extent of the spectrum. But there is more to color than meets the eye. Color is a perceptual as well as physical phenomenon. What is commonly called color-hue-is only one of three parameters. Another is the brightness of the signal-intensity. The third is the admixture of white -saturation. Change any one parameter enough, and the color looks different. (The hue-intensity-saturation model of color is one of a several explored through the years, and captures some of the basic characteristics of basic color perception.) To make matters worse, the parameters' relationship to what is perceived is nonlinear. At the same intensity, for example, yellow appears brighter than blue. Some of the perceptual principles involved have been implemented in software developed at IBM Corp.'s Thomas J. Watson Research Center, Yorktown Heights, NY. The module runs with IBM's visualization package Data Explorer and is called Pravda (for perceptual rule based architecture for visualizing data accurately)

Towards the conceptions of visualization language and visualization metaphor

VL Averbukh — 2006-06-07T19:07:58-00:00

An Extended Scatterplot Matrix and Case Studies in Information Visualization. Published as Diplomarbeit

Robert Voigt — 2006-06-07T15:26:30-00:00

(October 2002)

Often data is collected to find new useful information. One way to extract information out of data is information visualization. It utilizes the power of the human visual system to find for example patterns in the data. Many techniques for information visualization have been developed, for example scatterplots and parallel coordinates. These two basic techniques are used in three computer programs that were developed at the VRVis research center in Vienna, Austria. One of them is an extended scatterplot matrix. It has some new features that improve effectiveness with large datasets and categorical data. Another program uses scatterplots in three dimensions, and the third extends the concept of parallel coordinates with a number of advanced interaction techniques. These programs are used to perform realistic visualization scenarios with datasets from economy and engineering. It is shown that information visualization can successfully be used to explore and analyze datasets and for presentation purposes.

As You Like It: Tailorable Information Visualization

Michael Averbuch — 2006-06-07T15:18:08-00:00

Information visualization tools have traditionally implemented a set of pre-defined visual displays. We describe the DOODLE Visualization Tool, which is interactive and supports visualizations specified by the user with a visual constraint-based language. The main modules of the tool comprise the syntax-directed user interface, the parser for the user's specification, and the constraint solver. The strengths of our approach include the expressiveness of the visual language, the efficiency of the constraint solver, and the overall flexibility and extensibility of the framework. The user interface is implemented using Java and is available on the WWW.

The Sciences of the Artificial - 3rd Edition

Herbert Simon — 2005-03-30T19:06:01-00:00

(01 October 1996)

"People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller, <i>Complex Information Processing</i> <P>Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial intelligence adds a chapter that sorts out the current themes and tools -- chaos, adaptive systems, genetic algorithms -- for analyzing complexity and complex systems. There are updates throughout the book as well. These take into account important advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. The chapter "Economic Reality" has also been revised to reflect a change in emphasis in Simon's thinking about the respective roles of organizations and markets in economic systems.

Low-Level Components of Analytic Activity in Information Visualization

Robert Amar — 2006-06-07T00:14:17-00:00

(2005), 15.

Existing system-level taxonomies of visualization tasks are geared more towards the design of particular representations than the facilitation of user analytic activity. We present a set of ten low-level analysis tasks that largely capture people’s activities while employing information visualization tools for understanding data. To help develop these tasks, we collected nearly 200 sample questions from students about how they would analyze five particular data sets from different domains. The questions, while not being totally comprehensive, illustrated the sheer variety of analytic questions typically posed by users when employing information visualization systems. We hope that the presented set of tasks is useful for information visualization system designers as a kind of common substrate to discuss the relative analytic capabilities of the systems. Further, the tasks may provide a form of checklist for system designers.

Knowledge precepts for design and evaluation of information visualizations

RA Amar — 2006-05-11T21:27:12-00:00

Visualization and Computer Graphics, IEEE Transactions on, Vol. 11, No. 4. (2005), pp. 432-442.

The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.

A Knowledge Task-Based Framework for Design and Evaluation of Information Visualizations

Robert Amar — 2005-02-22T22:10:15-00:00

(2004), pp. 143-150.

An insight-based methodology for evaluating bioinformatics visualizations

P Saraiya — 2006-06-07T00:06:44-00:00

Visualization and Computer Graphics, IEEE Transactions on, Vol. 11, No. 4. (2005), pp. 443-456.

High-throughput experiments, such as gene expression microarrays in the life sciences, result in very large data sets. In response, a wide variety of visualization tools have been created to facilitate data analysis. A primary purpose of these tools is to provide biologically relevant insight into the data. Typically, visualizations are evaluated in controlled studies that measure user performance on predetermined tasks or using heuristics and expert reviews. To evaluate and rank bioinformatics visualizations based on real-world data analysis scenarios, we developed a more relevant evaluation method that focuses on data insight. This paper presents several characteristics of insight that enabled us to recognize and quantify it in open-ended user tests. Using these characteristics, we evaluated five microarray visualization tools on the amount and types of insight they provide and the time it takes to acquire it. The results of the study guide biologists in selecting a visualization tool based on the type of their microarray data, visualization designers on the key role of user interaction techniques, and evaluators on a new approach for evaluating the effectiveness of visualizations for providing insight. Though we used the method to analyze bioinformatics visualizations, it can be applied to other domains.

Extending taxonomic visualisation to incorporate synonymy and structural markers

Martin Graham — 2005-08-31T11:50:18-00:00

Information Visualization, Vol. 4, No. 3. (2005), pp. 206-223.

User Interface Design and Evaluation

Sturtz — 2006-05-19T04:03:06-00:00

Information Visualization, Vol. 5, No. 1. (2006), pp. 77-78.

Evaluating visualizations: do expert reviews work?

M Tory — 2006-06-06T21:37:04-00:00

Computer Graphics and Applications, IEEE, Vol. 25, No. 5. (2005), pp. 8-11.

Visualization research generates beautiful images and impressive interactive systems. Emphasis on evaluating visualizations is growing. Researchers have successfully used alternative evaluation techniques in human-computer interaction (HCI), including focus groups, field studies, and expert reviews. These methods tend to produce qualitative results and require fewer participants than controlled experiments. In this article, we focus on expert reviews that we used for the applications. We commonly use expert reviews to assess interface usability. Expert reviews can generate valuable feedback on visualization tools. We recommend i) including experts with experience in data display as well as usability, and ii) developing heuristics based on visualization guidelines as well as usability guidelines. Expert reviews should not be used exclusively, since experts might not hilly predict end-user actions. Furthermore, we encourage more experimentation with this technique, particularly to develop a good set of visualization heuristics and to compare it with other methods.

Evaluating an intelligent graphical user interface by comparison with human experts

M Virvou — 2004-12-28T16:34:23-00:00

Knowledge-Based Systems, Vol. 17, No. 1. (January 2004), pp. 31-37.

A review and taxonomy of distortion-oriented presentation techniques

YK Leung — 2006-05-31T22:20:45-00:00

ACM Trans. Comput.-Hum. Interact., Vol. 1, No. 2. (June 1994), pp. 126-160.

A Classification Scheme for Lens Technique

Henning Griethe — 2006-06-06T21:32:01-00:00

(2005), pp. 89-92.

Graph Visualization and Navigation in Information Visualization: A Survey

Herman — 2005-06-06T20:15:53-00:00

IEEE Transactions on Visualization and Computer Graphics, Vol. 6, No. 1. (ash 2000), pp. 24-43.

This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as web browsing, state--transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different...

A Cognitive Classification Framework for 3-Dimensional Information Visualization

U Wiss — 2006-06-06T21:24:50-00:00

No. Research report LTU-TR--1998/4--SE. (1998)

A taxonomy of glyph placement strategies for multidimensional data visualization

Matthew Ward — 2006-06-06T21:22:51-00:00

Information Visualization, Vol. 1, No. 3. (2002), pp. 194-210.

Glyphs are graphical entities that convey one or more data values via attributes such as shape, size, color, and position. They have been widely used in the visualization of data and information, and are especially well suited for displaying complex, multivariate data sets. The placement or layout of glyphs on a display can communicate significant information regarding the data values themselves as well as relationships between data points, and a wide assortment of placement strategies have been developed to date. Methods range from simply using data dimensions as positional attributes to basing placement on implicit or explicit structure within the data set. This paper presents an overview of multivariate glyphs, a list of issues regarding the layout of glyphs, and a comprehensive taxonomy of placement strategies to assist the visualization designer in selecting the technique most suitable to his or her data and task. Examples, strengths, weaknesses, and design considerations are given for each category of technique. We conclude with some general guidelines for selecting a placement strategy, along with a brief description of some of our future research directions.

A Taxonomy of Multiple Window Coordinations

Chris North — 2006-06-06T21:06:11-00:00

No. CS-TR-3854. (1997)

A Taxonomy of visualization techniques for simulation in production and logistics

Sigrid Wenzel — 2006-06-06T21:04:08-00:00

(2003), pp. 729-736.

A Taxonomy of Browsing Mehthods: Approaches to the 'Lost in Concept Space' Problem

Flip Korn — 2006-06-06T21:00:06-00:00

(1996)

Visualization Process of Temporal Data

Chaouki Daassi — 2006-06-06T20:56:12-00:00

LECTURE NOTES IN COMPUTER SCIENCE, No. 3180. (2004), pp. 914-924.

Image-Browser Taxonomy and Guidelines for Designers

Catherine Plaisant — 2006-06-06T20:51:34-00:00

IEEE Software, Vol. 12, No. 2. (March 1995), pp. 21-32.

A taxonomy of graphical presentation

Robert Spence — 2006-05-25T21:49:33-00:00

(1993), pp. 113-114.

An Interaction View on Information Visualization

Robert Kosara — 2006-06-06T20:48:31-00:00

(2003), pp. 123-137.

A taxonomy of visualization techniques using the data state reference model

E Chi — 2005-03-01T21:48:50-00:00

Information Visualization, 2000. InfoVis 2000. IEEE Symposium on (2000), pp. 69-75.

In previous work, researchers have attempted to construct taxonomies of information visualization techniques by examining the data domains that are compatible with these techniques. This is useful because implementers can quickly identify various techniques that can be applied to their domain of interest. However, these taxonomies do not help the implementers understand how to apply and implement these techniques. The author extends and proposes a new way to taxonomize information visualization techniques by using the Data State Model (E.H. Chi and J.T. Reidl, 1998). In fact, as the taxonomic analysis in the paper will show, many of the techniques share similar operating steps that can easily be reused. The paper shows that the Data State Model not only helps researchers understand the space of design, but also helps implementers understand how information visualization techniques can be applied more broadly

Data characterization for intelligent graphics presentation

Steven Roth — 2005-05-16T18:23:47-00:00

(1990), pp. 193-200.

A problem-oriented classification of visualization techniques

Stephen Wehrend — 2005-02-22T23:48:04-00:00

(1990), pp. 139-143.

Evaluating visualizations: using a taxonomic guide

E Morse — 2005-02-22T22:35:49-00:00

International Journal of Human-Computer Studies, Vol. 53, No. 5. (November 2000), pp. 637-662.

Although visualizations are components of many information interfaces, testing of these visual elements is rarely undertaken except as a part of overall usability testing. For this reason, it is unclear what role, if any, visualizations actually perform. Our method involves the creation of simple visual prototypes and task sets based on a visual taxonomy which allows testing of the visualization in isolation from the rest of the system. By defining tests using a visual taxonomy rather than customary tasks from the application domain, our method circumvents the problems of restricting evaluation of newer more capable systems to only those tasks which might be accomplished with older, less capable ones. This paper will discuss methods for exhaustively testing the capabilities of a visualization by mapping from a domain-independent taxonomy of visual tasks to a specific domain, i.e. information retrieval. Experimental results are presented illustrating this approach to determining the role visualizations may play in supporting users in information-seeking environments. Our methods could easily be extended to other domains including data visualization.

The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations

Ben Shneiderman — 2005-02-22T22:13:10-00:00

(1996)

Taxonomy of visualization techniques and systems - concerns between users and developers are different

Chengzhi Qin — 2006-06-06T20:28:03-00:00

(2003)

A unified taxonomic framework for information visualization

Darius Pfitzner — 2005-02-22T22:33:09-00:00

(2003), pp. 57-66.

prefuse: a toolkit for interactive information visualization

Jeffrey Heer — 2005-06-01T19:26:04-00:00

(2005), pp. 421-430.

Information visualization and its application to medicine

Luca Chittaro — 2006-02-07T22:24:31-00:00

Artificial Intelligence in Medicine, Vol. 22, No. 2. (2001), pp. 81-88.

This paper provides an introduction to the field of Information Visualization (IV) and a discussion of its application to medical systems. More specifically, it aims at: (i) defining what IV is and what are its goals, (ii) highlighting the similarities and differences between IV and traditional medical imaging, (iii) illustrating the potential of IV for medical applications by examining several examples of implemented systems, (iv) giving some general indications about the purposes and the...

The challenge of information visualization evaluation

Catherine Plaisant — 2005-02-22T22:19:19-00:00

(2004), pp. 109-116.

Rethinking Visualization: A High-Level Taxonomy

M Tory — 2006-05-11T19:41:18-00:00

Information Visualization, 2004. INFOVIS 2004. IEEE Symposium on (2004), pp. 151-158.

We present a novel high-level visualization taxonomy. Our taxonomy classifies visualization algorithms rather than data. Algorithms are categorized based on the assumptions they make about the data being visualized; we call this set of assumptions the design model. Because our taxonomy is based on design models, it is more flexible than existing taxonomies and considers the user’s conceptual model, emphasizing the human aspect of visualization. Design models are classified according to whether they are discrete or continuous and by how much the algorithm designer chooses display attributes such as spatialization, timing, colour, and transparency. This novel approach provides an alternative view of the visualization field that helps explain how traditional divisions (e.g., information and scientific visualization) relate and overlap, and that may inspire research ideas in hybrid visualization areas.

Dust Magnet: multivariate information visualization using a magnet metaphor

Ji Yi — 2005-12-11T23:38:08-00:00

Information Visualization, Vol. 4, No. 4. (2005), pp. 239-256.

Decision Support System induced guidance for model formulation and solution

Reza Barkhi — 2006-02-14T04:38:59-00:00

Decision Support Systems, Vol. 40, No. 2. (August 2005), pp. 269-281.

One of the critical functions of Decision Support System (DSS) is to provide system induced decision guidance for proper model formulation and solution. We show how to incorporate this type of system induced decision guidance into the design of the next generation of DSS. We suggest that a DSS should make decisions, or at least recommendations, regarding what models should be executed to solve problems most effectively and this information should be generated inductively and used deductively. This information then becomes the meta-model to induce the user to make appropriate choices. We provide an example that will illustrate how two specific problem characteristics, namely the tightness of constraints and the linearity of constraints, influence the solution quality and solution times for a specific class of test problems. We argue that a DSS should execute different formulations of the problem that lead to satisficing solutions guiding DSS users in finding the best approach to solve complex problems.

Spatial decision support for assisted housing mobility counseling

Michael Johnson — 2006-02-14T04:36:09-00:00

Decision Support Systems, Vol. 41, No. 1. (November 2005), pp. 296-312.

This paper presents a prototype spatial decision support system (SDSS) that enables clients in the Housing Choice Voucher Program to make better decisions about neighborhoods in which to search for housing and specific units to evaluate for occupancy. Application requirements are based on field research establishing limitations of housing counselors to provide detailed assistance to clients, and the capability of clients to use a spatial decision support system. Decision opportunities are identified using value-focused thinking and spatial analysis. Specific destination alternatives are ranked using a destination choice algorithm based on multicriteria decision models that incorporates alternative relocation strategies.

A.V. Lotov, V.A. Bushenkov and G.K. Kamenev, Interactive Decision Maps: Approximation and Visualization of Pareto Frontier, Kluwer Academic Publishers (2004) ISBN 1-4020-7631-2.

Kaisa Miettinen — 2006-02-14T04:28:44-00:00

European Journal of Operational Research, Vol. 168, No. 1. (1 January 2006), pp. 272-274.

Information and knowledge visualization in development and use of a management information system (MIS) for DaimlerChrysler - a visualized dialogue and participation process

HJ Frank — 2005-01-20T00:29:54-00:00

Knowledge and Information Visualization (2005), pp. 364-384.

Exploratory Data Analysis for Complex Models

Andrew Gelman — 2004-12-28T17:03:21-00:00

Journal of Computational & Graphical Statistics, Vol. 13, No. 4., 755.

"Exploratory" and "confirmatory" data analysis can both be viewed as methods for comparing observed data to what would be obtained under an implicit or explicit statistical model. For example, many of Tukey's methods can be interpreted as checks against hypothetical linear models and Poisson distributions. In more complex situations, Bayesian methods can be useful for constructing reference distributions for various plots that are useful in exploratory data analysis. This article proposes an approach to unify exploratory data analysis with more formal statistical methods based on probability models. These ideas are developed in the context of examples from fields including psychology, medicine, and social science.

The New Science of Management Decision

Herbert Simon — 2006-01-10T19:54:49-00:00

(1977)

The Role of Visual Perception in Data Visualization

M Dastani — 2004-12-28T16:35:28-00:00

Journal of Visual Languages & Computing, Vol. 13, No. 6. (December 2002), pp. 601-622.

Prototypes and Paratypes: Designing Mobile and Ubiquitous Computing Applications

GD Abowd — 2005-11-30T13:48:23-00:00

Pervasive Computing, IEEE, Vol. 4, No. 4. (2005), pp. 67-73.

User-centered design has long made use of physical, contextual, and functional prototyping techniques. Combining these techniques to generate compound prototypes and situated experience prototypes, or "paratypes," can be particularly useful for mobile and ubiquitous computing applications prior to full-scale development and deployment efforts.This article is part of a special issue on rapid prototyping.

The future of mobile technology and mobile wireless computing

Jim Hart — 2004-12-28T18:13:58-00:00

Campus-Wide Information Systems, Vol. 21, No. 5., 201.

A Model-Based Visualization Taxonomy

M Tory — 2005-03-14T08:12:54-00:00

(2002)

Frameworks for organizing literature and ideas in visualization are valuable since they allow us to gain a higherlevel understanding of the state of visualization research. Current taxonomies of visualization techniques are problematic because the terminology is vague. We present a new taxonomy based on models of a data set rather than attributes of the data itself. This method addresses several problems with existing classification schemes and generates less ambiguous visualization categories.

Informed spatial decisions through coordinated views

N Andrienko — 2005-03-03T09:53:20-00:00

Information Visualization, Vol. 2, No. 4. (December 2003), pp. 270-285.

According to a commonly accepted view, the process of decision making comprises three major phases: intelligence (situation analysis and problem recognition), design (finding possible variants of problem solution), and choice (evaluation of the options and selection of the most appropriate ones). It is widely recognised that exploratory data visualisation is very helpful during the first phase of the decision-making process, while the other phases require different software tools. In particular, the choice phase is typically supported by various computational methods that find appropriate trade-offs among multiple conflicting criteria taking into account user-specified priorities. Visualisation plays a limited role: in the best case, it is used to represent the final results of the computations. We argue that conscious, well-substantiated choice requires a more extensive use of exploratory visualisation facilities, which need to be properly coordinated with the computational multi-criteria decision support methods. Extremely important is a high degree of user interactivity, which allows the user to probe the robustness and quality of computationally derived solutions. We suggest several mechanisms for linking and coordinating visual exploratory tools with two types of computational methods differing in the sort of output they produce. We demonstrate the use of this dynamic link with an example of a real spatially related decision problem.Information Visualization (2003) 2, 270–285. doi:10.1057/palgrave.ivs.9500058