| |  | Groth, D. P. | Information provenance and the knowledge rediscovery problem read moreAbstract: Visualizations leverage innate human capabilities for recognizing interesting aspects of data. Even if users might agree on what is interesting about a visualization, the steps that they use in the knowledge discovery process may be significantly different. This results in an inability to effectively recreate the exact conditions of the discovery process, which we call the knowledge rediscovery problem. Because we cannot expect a user to fully document each of their interactions, there is a need for visualization systems to maintain user trace data in a way that enhances a user's ability to communicate what they found to be interesting, as well as how they found it. We present a model for representing user interactions that articulates with a corresponding set of annotations, or observations that are made during the exploration. Such ability is critical to addressing the knowledge rediscovery problem, and is a fundamental component for systems that must provide information provenance. | 2004 |
| | | Chen, Chaomei | Visualizing a knowledge domain's intellectual structure read moreAbstract: To make knowledge visualizations clear and easy to interpret, we have developed a method that extends and transforms traditional author co-citation analysis by extracting structural patterns from the scientific literature and representing them in a 3D knowledge landscape | 2001 |
| | | Shneiderman, B. | The eyes have it: a task by data type taxonomy for information read moreAbstract: A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts) | 1996 |
| | | Aiken, Alexander | Tioga-2: A Direct Manipulation Database Visualization Environment read moreAbstract: The paper reports on user experience with Tioga, a DBMS centric visualization tool developed at Berkeley. Based on this experience, we have designed Tioga-2 as a direct manipulation system that is more powerful and much easier to program. A detailed design of the revised system is presented, together with an extensive example of its application | 1996 |
| | | Beshers, C. | AutoVisual: rule-based design of interactive multivariate read moreAbstract: An extension to the n-Vision visualization system, which provides users with a 3D virtual world within which they can visualize and manipulate representations of multivariate relations is discussed. The extension, AutoVisual, is rule based system that eliminates the difficulty in choosing among the many alternative when designing visualizations. AutoVisual designs interactive virtual worlds for visualizing and exploring multivariate relations. It is guided by user-specified visualization tasks and a rule base of design principles. AutoVisual's visualization techniques and the visualization tasks it handles are described. Example visualizations AutoVisual has generated for two problem domains are discussed | 1993 |
| | | Springmeyer, Rebecca R. | A characterization of the scientific data analysis process read moreAbstract: Extensible scientific visualization tools are often offered as data analysis tools. While images may be the goal of visualization, insight is the goal of analysis. Visualization tools often fail to reflect this fact both in functionality and in their user interfaces, which typically focus on graphics and programming concepts rather than on concepts more meaningful to end-user scientists. This paper presents a characterization which shows how data visualization fits into the broader process of scientific data analysis. We conducted an empirical study, observing scientists from several disciplines while they analyzed their own data. Examination of the observations exposed process elements outside conventional image viewing. For example, analysts queried for quantitative information, made a variety of comparisons, applied math, managed data, and kept records. The characterization of scientific data analysis reveals activity beyond that traditionally supported by computer. It offers an understanding which has the potential to be applied to many future designs, and suggests specific recommendations for improving the support of this important aspect of scientific computing | 1992 |
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