Keller, P. & Keller, M. 1992, Visual Cues: Practical Data Visualization. Los Alamitos, CA: IEEE Computer Society Press.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....A perspective projection of a 3D object gives a planar description of how the object looks from a particular view point in space, and we are interested in making that projection as good as possible. The topic of this paper is hence related to the much broader domain of Scienti c Visualization [1, 2], the discipline concerned with helping us to better visualize information, objects and processes in their fullest generality. We consider objects which may be abstracted as sets of segments in space. Ambiguities and loss of information can arise in the image (the perspective projection) and one ....

P.R. Keller and M.M. Keller, Visual Cues: Practical Data Visualization, IEEE Computer Society Press, 1993.

....or implicitly (by human eye brain fusion) into a 3D surface. By turning the grid of heights into a surface, the viewer can better look at the dataset as a whole, discerning trends and patterns and discovering anomalies. The most common use for height fields is to visualize terrain surfaces ([KELLER93], WOLFF93] In this case, X and Y are longitude and latitude, and the height is elevation above or below a base reference. It is from this application that the synonym for height field, elevation grid, is derived. Height fields are also useful for visualizing scalar values mapped to a data ....

....that the synonym for height field, elevation grid, is derived. Height fields are also useful for visualizing scalar values mapped to a data surface. Examples of this sort of application include stresses in plates, strain energy density, temperature, pressure, and ozone density ( KAUFMANN93] [KELLER93], WOLFF93] A graphical display of a height field data suffers from all the same problems as the display of any other 3D surface. The image itself is displayed on a flat piece of glass. It is not really 3D it only can be made to appear 3D. Therefore, we must play a variety of graphics ....

Peter Keller and Mary Keller, Visual Cues: Practical Data Visualization, IEEE Press, 1993.

....Therefore it is desirable to obtain 2 D representations of our data that make it easy for us to discover the structure in the real data in 3 D. Our methods for computing non degenerate projections are also applicable to this family of problems that falls in the areas of scientific visualization [11, 14], computer graphics [3, 13] and computer vision [4, 15] This paper is structured as follows. In Section 2 we give algorithms for computing perspective projections of a set of points such that all points in the projection have distinct x coordinates; have both distinct x and y coordinates. In ....

P.R. Keller and M.M. Keller, "Visual Cues: Practical Data Visualization", IEEE Computer Society Press, 1993.

....graphical representation of the information collected, because this provides a comfortable inspection of the details. Examples for performance visualization tools are ParaGraph [19, 20, 21] MulTVision [18] Traceview [28] and PV [26] More information on visualization techniques can be found in [25] and [36] Monitoring tools include ParaGraph [19, 20, 21] Xab [10] and Paradyn [30] The instrumentation is usually build into the runtime system or the operating system, see [23] for an overview. In the area of parallelizing compilers, there is considerable research effort to build modeling ....

P. Keller and M. Keller. Visual Cues: Practical Data Visualization. IEEE Press, 1993.

.... represented with 0 and 90, respectively; c) both fields displayed in a single image, overlapping values show as elements that look like plus signs (c) x (a) b) Graphics Interface 2000: Research Paper Submission 3 A good overview of some of these techniques is presented in Keller and Keller [10]. Our work is most similar to methods that use textures or glyphs to represent multiple attribute values at a single spatial location. We therefore focus our study of previous work on this broad area. Texture has been studied extensively in the computer vision, computer graphics, and cognitive ....

KELLER, P. AND KELLER, M. Visual cues: Practical data visualization. IEEE Computer Society Press, Los Alamitos, California, 1991.

....scientific databases. These developments involve areas which at first glance seem to be distinct and disjoint: # Multimedia. In its fullest and most complete form, sci entific data is multimedia in nature. It is fully visual, frequently three dimensional, and spans the dimension of time [32]. Much work has focused in this area, and our work proceeds in a similar direction as in [31] 38] 12] 27] # Simulation and validation. Harreld [27] and Anzai et al. 3] are evidence of an increasing need to integrate simulation and database technologies. One potential benefit of this ....

P.R. Keller and M.M. Keller, Visual Cues: Practical Data Visualization, IEEE CS Press, Los Alamitos, Calif., 1993.

....technique should be applied, and on which kind of graphical primitive the data should be mapped. This problem is of great importance, since an unsuitable visualization may lead to wrong conclusions. Certain rules and rule systems exist for the support of a user during the visualization process [1, 18, 2, 15, 5], but not only is their scope generally very limited, they are also not available as an on line help system and are consequently not appropriate as a support for daily work. Moreover, they are not appropriate for modern, time dependent visualization techniques such as animation. On the other hand, ....

Keller, Peter R., and Keller, Mary M.: Visual Cues - Practical Data Visualization. IEEE Computer Society Press, Los Alamitos, 1993.

....the user in customizing a prototype module network for his her desired applications by showing execution examples involving datasets of the same type. The next section provides an overview of the GADGET guidance mechanism, with an emphasis on the Wehrend s taxonomy of visualization techniques [32, 14] as the primary component of the underlying visineers heuristics and expertise. Section 3 is devoted to the GADGET s knowledge base, including the conceptual schema design using an object oriented modeling methodology. A translated relational schema and an example operation with a set of ....

.... visineers knowledge representation 1 , the GADGET system relies heavily on the goal oriented taxonomy of visualization techniques, which was originally proposed by Wehrend, et al. 32] The feasibility of this approach was demonstrated by indexing a hundred of collected visualization pieces in [14]. First, Section 2.1 gives a formal review of the taxonomy based method for selecting visualization techniques. Then, Section 2.2 specifies other requirements for the GADGET s user guidance capabilities. 2.1 Taxonomy Based Selection Method The Wehrend s taxonomy hypothesizes that identification ....

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Peter R. Keller and Mary M. Keller. Visual Cues -- Practical Data Visualization. IEEE Computer Society Press, 1993. ISBN 0-8186-3102-3.

....of graphical primitive the data should be mapped. This problem is of great impact since an unsuitable visualization may lead to wrong conclusions. Certain rules and rule systems exist which try to support the user during the visualization process (i.e. Tuft 83] Bert 83] Clev 85] SeIg 90] KeKe 93] but their scope is generally very limited, they are not available as on line help system and are consequently not appropriate as a support for daily work. Moreover, they are not appropriate for modern, time dependent visualization techniques such as animation. Besides that, there exist a ....

Keller, Peter R., and Keller, Mary M.: Visual Cues - Practical Data Visualization, IEEE Computer Society Press, Los Alamitos, 1993

....visualization approaches, then uncertainty visualization approaches, and finally presenting several new methods for uncertainty visualization. 2 Classification of Methods To classify uncertainty visualization approaches, we first consider more general classifications. Keller and Keller [KK93] classify visualization by using a taxonomy of visualization goals. Tufte [Tuf83] classifies visualizations by developing evaluation and analysis methods such as data ink maximization. Carswell [Car92] and Cleveland [Cle85, CM86] use evaluation as a basis for the theory of specifiers, that ....

Peter Keller and Mary Keller. Visual Cues: Practical Data Visualization. IEEE Computer Society Press, 1993.

....intimately on both the phenomenon being visualized, and the purposes for which the visualization is being used. Consequently, we believe that highly specialized visualization languages should be developed on a case1 For an overview, see (Price, Baecker Small 1993) 2 For an overview, see (Keller Keller 1993). 3 Here, it is interesting to note that in the case of software visualization, the phenomenon of interest is itself a computer program. Thus, we get the computational model of the phenomenon for free. VIZ User s Manual 7 Overview of VIZ by case basis. On the other hand, we do believe that ....

Keller, P.R. & Keller, M.M. (1993). Visual Cues: Practical data visualization. Los Alamitos, CA: IEEE CS Press.

....such as paper or a computer graphics screen, on which to display a necessarily incomplete representation or picture of the objects we are interested in. Therefore it is desirable to obtain 2 D representations of our objects that approximate the real objects as faithfully as possible in some sense [Kel93], Gal95] A sub field of visualization closely related to the class of problems considered here is graph drawing [DBETT94] One of the archetypal problems in graph drawing consists of asking, for a given graph, a nice drawing of it. A graph in this context is not a rigid object in 3 D space but ....

M.M. Keller, and P.R. Keller. Visual Cues: Practical Data Visualization. IEEE Computer Society Press, 1993.

....paper or a computer graphics screen, on which to display a necessarily incomplete representation or picture of the objects we are interested in. Therefore it is desirable to obtain 2 D representations of our objects that approximate the real objects as faithfully as possible in some sense [Kel93], Gal95] A sub field of visualization closely related to the class of problems considered here is graph drawing [DBETT94] One of the archetypal problems in graph drawing consists of asking, for a given graph, a nice drawing of it. A graph in this context is not a rigid object in 3 D space but ....

M.M. Keller, and P.R. Keller. Visual Cues: Practical Data Visualization. IEEE Computer Society Press, 1993.

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Keller, P. & Keller, M. 1992, Visual Cues: Practical Data Visualization. Los Alamitos, CA: IEEE Computer Society Press.

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P.R. Keller & M.M. Keller. Visual Cues: Practical Data Visualization. Piscataway, NJ: IEEE Press, 1993. P. 67.

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Keller, P. R. & Keller, M. M. (1993), Visual Cues - Practical Data Visualization, IEEE Computer Society Press, Los Alamitos, CA.

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Keller, P.R. and Keller M.M. Visual Cues: Practical Data Visualization . Manning Publications Co., Greenwish CT, 1993.

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P.R. Keller & M.M. Keller. Visual Cues: Practical Data Visualization. Piscataway, NJ: IEEE Press, 1993. P. 67.

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P. Keller, and M. M. Keller, Visual Cues: Practical Data Visualization, IEEE Computer Society Press, 1993.

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P. R. Keller, M. M. Keller. Visual Cues: Practical Data Visualization. IEEE Computer Society Press, Los Alamitos CA, 1992. 21

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