Person:

Foley, James D.

Permanent Link

https://hdl.handle.net/1853/71261

Associated Organization(s)

Organizational Unit

School of Interactive Computing

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Publication Search Results

Now showing 1 - 10 of 21

AASERT : scalable user interfaces

(Georgia Institute of Technology, 1999) Foley, James D.
Providing access to graphical user interfaces : the mercator project

(Georgia Institute of Technology, 1996) Mynatt, Elizabeth D. ; Foley, James D.
Visualizing Complex Hypermedia Networks through Multiple Hierarchical Views

(Georgia Institute of Technology, 1995) Mukherjea, Sougata ; Foley, James D. ; Hudson, Scott E.

Our work concerns visualizing the information space of hypermedia systems using multiple hierarchical views. Although overview diagrams are useful for helping the user to navigate in a hypermedia system, for any real-world system they become too complicated and large to be really useful. This is because these diagrams represent complex network structures which are very difficult to visualize and comprehend. On the other hand, effective visualizations of hierarchies have been developed. Our strategy is to provide the user with different hierarchies, each giving a different perspective to the underlying information space, to help the user better comprehend the information. We propose an algorithm based on content and structural analysis to form hierarchies from hypermedia networks. The algorithm is automatic but can be guided by the user. The multiple hierarchies can be visualized in various ways. We give examples of the implementation of the algorithm on two hypermedia systems.
Visualizing the World-Wide Web with the Navigational View Builder

(Georgia Institute of Technology, 1995) Mukherjea, Sougata ; Foley, James D.

Overview diagrams are one of the best tools for orientation and navigation in hypermedia systems. However, constructing effective overview diagrams is a challenging task. This paper describes the Navigational View Builder, a tool which allows the user to interactively create useful visualizations of the information space. It uses four strategies to form effective views. These are binding, clustering, filtering and hierarchization. These strategies use a combination of structural and content analysis of the underlying space for forming the visualizations. This paper discusses these strategies and shows how they can be applied for forming visualizations for the World-Wide Web.
A Report of the NSF/IRIS Workshop

(Georgia Institute of Technology, 1995) Berwick, Robert C. ; Carroll, John M. (John Millar) ; Connolly, Chris ; Foley, James D. ; Fox, Edward A. (Edward Alan) ; Imielinski, Tomasz ; Subrahmanian, V.S.

The Information, Robotics, and Intelligent Systems Division (IRIS) of the National Science Foundation commissioned a workshop to provide a set of recommendations concerning: 1. Opportunities for NSF's use of WWW for information delivery to the public and research communities. 2. Research which NSF in general and IRIS in particular should consider undertaking with respect to the WWW, its accessibility, and its usability. 3. Use of WWW as an experimental platform for collaborative efforts in the IRIS and computer science research communities, including potential enhancements to WWW in support of such collaborations. This report summarizes a set of strategic recommendations for NSF and elaborates a recommended research agenda surrounding the Web.
A Pure Reasoning Engine for Programming by Demonstration

(Georgia Institute of Technology, 1994) Frank, Martin Robert ; Foley, James D.

We present an inference engine that can be used for creating Programming By Demonstration systems. The class of systems addressed are those which infer a state change description from examples of state. The engine can easily be incorporated into an existing design environment that provides an interactive object editor. The main design goals of the inference engine are responsiveness and generality. All demonstrational systems must respond quickly because of their interactive use. They should also be general- they should be able to make inferences for any attribute that the user may want to define by demonstration, and they should be able to treat any other attributes as parameters of this definition. The first goal, responsiveness, is best accommodated by limiting the number of attributes that the inference engine takes into consideration. This, however, is in obvious conflict with the second goal, generality. This conflict is intrinsic to the class of demonstrational system described above. The challenge is to find an algorithm which responds quickly, but does not heuristically limit the number of objects it looks at. We present such an algorithm in this paper. A companion paper describes Inference Bear, an actual demonstrational system that we have built using this inference engine and an existing user interface builder.
Grouping and Ordering User Interface Components

(Georgia Institute of Technology, 1994) Gray, Mark H. ; Foley, James D. ; Mullet, Kevin E.

In automatically generating a user interface from a model of the target application, many factors that affect the resulting interface's quality must be considered. Any available semantic information that can improve the interface should be used. Application actions or action parameters may be related in ways that affect placement of their associated controls in dialogue boxes. Two relationships considered here are grouping and ordering. Grouped objects should appear together, possibly visually separated from other controls, and controls which have a logical sequential ordering should appear in that order. We present an algorithm for creating an ordering of controls which correctly satisfies these constraints.
Inference Bear: Inferring Behavior from Before and After Snapshots

(Georgia Institute of Technology, 1994) Frank, Martin Robert ; Foley, James D.

We present Inference Bear (Inference Based On Before And After Snapshots) which lets users build functional graphical user interfaces by demonstration. Inference Bear is the first Programming By Demonstration system based on the abstract inference engine described in [5]. Among other things, Inference Bear lets you align, center, move, resize, create, and delete user interface elements by demonstration. Its most notable feature is that it does not use domain knowledge in its inferencing.
Next-Generation Data Visualization Tools

(Georgia Institute of Technology, 1994) Ribarsky, William ; Foley, James D.

Scientific data visualization has finally come of age as an important and accepted discipline. While scientists have been using computer graphics to visualize experimental data and computational results for at least 30 years, recent improvements in cost/performance of graphics workstations, more readily available software, and the new-found name "scientific data visualization" have solidified the discipline. Many thousands of scientists regularly use visualizations as part of their work. Our thesis is that scientists are forced to work too hard to create these visualizations, but that the evolving set of visualization tools can greatly reduce the requisite effort. Further, these tools, and those of the next generation, will open significant new ways to understand increasingly complex and large datasets. Principal causes for this will be the increased interactivity that the tools will naturally offer and the need for designs based on careful consideration of scientific and engineering analysis requirements.
Model-Based User Interface Design by Example and by Interview

(Georgia Institute of Technology, 1993) Frank, Martin Robert ; Foley, James D.

Model-based user interface design is centered around a description of application objects and operations at a level of abstraction higher than that of code. A good model can be used to support multiple interfaces, help separate interface and application, describe input sequencing in a simple way, check consistency and completeness of the interface, evaluate the interface's speed-of-use, generate context-specific help and assist in designing the interface. However, designers rarely use computer-supported application modelling today and prefer less formal approaches such as story boards of user interface prototypes. One reason is that available tools often use cryptic languages for the model specification. Another reason is that these tools force the designers to specify the application model before they can start working on the visual interface, which is their main area of expertise. We present the Interactive User Interface Design Environment (Interactive UIDE), a novel framework for concurrent development of the application model and the user interface which combines storyboarding and model-based interface design. We also present Albert, an intelligent component within this framework, which is able to infer an application model from a user interface and from an interview process with the designer.