C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In Proceedings Visualization '92, pages 202--209, 1992. 22  Home/Search   Document Not in Database   Summary   Related Articles   Check

....is accomplished via a visual programming interface to a dataflownetwork. Software systems suchasAVS from Application Visualization Systems Inc. 51] Iris Explorer from Silicon Graphics, and Visualization Data Explorer from IBM [52] have made this archetype popular for scientific visualization [53]. Our work has extended this paradigm into the realm of scientific computation. In SCIRun, the typical components of the computational paradigm geometric modeling, numerical analysis, and scientific visualization are integrated into a visual programming environment with the ....

C. Willams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Proceedings of Visualization '92, pages 202--209. IEEE Press, 1992.

....CPU SGI Challenge and the visualization is performed on an SGI Indigo2 High Impact workstation. The systems are connected through an ATM network. 5 Related Work Many research and development teams have designed and implemented interactive visualization environments. Williams, Rashure and Hanson [8] provide a framework to understand design tradeoffs when developing data flow based visualization systems. Gu, Vetter and Schwan [9] give a comprehensive annotated bibliography of many aspects of interactive program steering. Giving an in depth analysis of other visualization environments is ....

C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In Proceedings Visualization '92, pages 202--209, 1992.

....set V of vertices and a set E of edges, that is, pairs of vertices. Graphs are commonly used to model relations in computing, and many systems for manipulating graphs have recently been developed. Examples include CASE tools [51] knowledge representation systems [28] software visualization tools [50], and VLSI design systems [24] A graph drawing algorithm reads as input a combinatorial description of a graph, and produces as output a visual representation of the graph. Such algorithms aim to produce drawings which are easy to read and easy to remember. Many graph drawing algorithms have been ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....edge and has O(n 2 ) area, which is as good as existing results for classical graph drawings. 1 Introduction Clustered graphs are graphs with recursive clustering structures over the vertices (see Fig. 1) This type of clustering structure appears in many systems. Examples include CASE tools [19], management information systems [10] and VLSI design tools [8] For graphical representation, the clustering structure is represented by a simple region that contains the drawing of all the vertices which belong to that cluster. Algorithms for automatically drawing of clustered graphs are ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992. 10

....13 90 percent of the CPU time was taken by the simulation satellite. The remaining 10 percent was used by the other satellites. 7. COMPARISON WITH OTHER SYSTEMS. Many research and development teams have designed and implemented interactive visualization environments. Williams, Rashure and Hanson [4] provide a framework to understand design tradeoffs when developing data flow based visualization systems. Giving an in depth analysis of other visualization environments is outside the scope of this paper. Instead, we discuss only some issues that resemble those in the CSE . Many of the concepts ....

C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In Proceedings Visualization '92, pages 202--209, 1992.

....al. Drawing Clustered Graphs, JGAA, 3(4) 3 29 (1999) 4 1 Introduction Graphs are commonly used to model relational information in computing. Many software systems need a graph drawing function. Examples include CASE tools [50] management information systems [22] software visualization tools [49], and VLSI design tools [20] Graph drawing algorithms aim to produce drawings which are easy to read and easy to remember. Many graph drawing algorithms have been designed, analyzed, tested and used in visualization systems [7] With increasing complexity of the information that we want to ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....annotated bibliography on various aspects of interactive data visualization, including interactive program monitoring and steering. Instead of a extensive overview of related work, we discuss work dealing with issues that relate to our blackboard architecture. Williams, Rasure and Hanson [18] provide a framework to understand design tradeoffs when developing data flow based visualization systems. Data flow systems are attractive because of the similarities with the visualization pipeline: users can easily organize their simulation, filter, mapping and render modules in an intuitive ....

C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In A.E. Kaufman and G.M. Nielson, editors, Proceedings Visualization '92, pages 202--209, 1992.

....clusters are again divided into clusters and so on. One would like to embed the nodes in a cluster quite closely. In VLSI design one is interested to put nodes into the same cluster that belong to the same electronic unit (see for example [8] Other applications appear in software visualization [18] and in knowledge representation [9] The ideal case would be when the graph could be embedded into the plane, such that edges do not cross and the clusters look nicely . That means clusters should appear as connected areas without holes. One algorithm that recognizes clustered graphs with ....

C. Williams, J. Rasure, C. Hansen, The State of the Art of Visual Languages for Visualization, Visualization 92 (1992), pp. 202-209. 18

....of information that we want to visualize becomes larger, we need more structure on top of the classical graph model. Graphs with recursive clustering structures over the vertices are called clustered graphs (see Fig. 1) This type of structure appears in many systems. Examples include CASE tools [16], management information systems [8] and VLSI design tools [7] In two dimensional representations, the clustering structure is represented by region inclusions, i.e. a cluster is represented by a simple region that contains the drawing of all the vertices which belong to that cluster (see Fig. ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....Brisbane Figure 1: An example of a clustered graph. 1 Introduction Graphs are commonly used to model relational information in computing. Many software systems need a graph drawing function. Examples include CASE tools [50] management information systems [23] software visualization tools [49], and VLSI design tools [21] Graph drawing algorithms aim to produce drawings which are easy to read and easy to remember. Many graph drawing algorithms have been designed, analyzed, tested and used in visualization systems [3] With increasing complexity of the information that we want to ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 - 209, 1992.

....as they flow through modules in a network. Modules are required to fire as new data arrive. The granularity refers to the size of the data block that the module processes. In these systems, it is the same size as the data model (hence large) rather than being an atomic element of the data model [18]. Granularity may also refer to the size and complexity of the modules. Once again, in these systems they are large in the sense that they implement complete algorithms (e.g. mapping or filter modules) A drawback with this approach is that memory requirements become prohibitive and cause ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Proceedings: Visualization '92, pages 202 -- 209. IEEE Computer Society, 1992.

....vertices of G (see Fig. 1) Each partition is known as a cluster of a subset of the vertices of G. Clustering appears in the diagrams produced in a wide number of applications areas, such as software engineering [23] knowledge representation [12] idea organization [14] software visualization [22], VLSI design [10] and general divide and conquer problem solving methodologies. Note that the class of clustered graphs is a subset of the class of undirected compound graphs. a b c d e f g h i j k l m n p A B C D E ROOT Figure 1: An example of a Clustered Graph We extend the definitions of ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....functionality that the user cannot modify. Libraries [3, 4] lend themselves to a better flexibility providing access to visualization functions, but require programming knowledge. The interaction with the end user is then the responsibility of the programmer. Visual programming environments [5, 6, 7, 8, 9] based on data flow diagrams are more recent. They are excellent for prototyping, but not U language S E R interface User images Virtual calculations Extraction Graphic processor Graphic representation Graphic Figure 1: The structure of the visualization process. efficient enough with ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Arie E. Kaufman and Gregory M. Nielson, editors, Proceedings Visualization '92 (Boston, Massachusetts), pages 202--209, Los Alamitos, CA, October 1992. IEEE Computer Society Press.

....a large body of techniques for displaying relational information as diagrams has been developed in the same three decades. Diagrammatic user interfaces have been developed in the areas of visual programming and CASE tools, database schema representation, and for other CAD systems (see for example [3, 4, 5, 6, 7, 8]) Figure 1.1 illustrates some advantages of a graphical representation of complex relational information. The task of visualising relational information is to produce an easily interpretable Z.1 X.3.1 Y.2 Y.1 X.2 X.1 X.3 X Y Z X.1.2 X.1.1 X.2.2 X.2.3 X.2.1 X.3.4 X.3.6 X.3.3 X.3.7 X.3.2 X.3.5 ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Arie E. Kaufman and Gregory M. Nielson, editors, Visualization '92, pages 202--209, 1992.

....G and a recursive partitioning of the vertices of G. Each partition is known as a cluster of a subset of the vertices of G. Clustering appears in the diagrams produced in a wide number of applications areas, such as software engineering [22] knowledge representation [14] software visualization [21], idea organization [15] VLSI design [10] and general divide and conquer problem solving methodologies. Planarity is a much studied area for classical graphs. For example, the problem of minimizing edge crossings is proved to be NP hard [8] However, efficient algorithms for testing whether a ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....element of current software systems. Examples include CASE tools [WBWW90, RP87, Koi92, RMD89] database design systems [Kam89b, RBB 84, RBF 87, BNTT85, BT86, Kaw75] VLSI design systems [Har88, BBB 86, BLSV92] network design systems [KK88, KMG88, TSS96] visual programming interfaces [WRH92, NFS 86, CE95, EZ96a, EZ95, EZ96b] and program comprehension and reverse engineering systems [SV92, GKNV92, PSTS91] Current systems mostly use graphs to model relational structures: the entities are vertices (or nodes) and the relationships are edges (or links) For example [EMar] Figure ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....window. DESIGN ISSUES Execution model In coarse grained data flow environments such as AVS and Explorer, the modules of the visual program network consume and produce blocks of data. These environments are coarse grained because the block size is the same as an instance of the data model [11]. A problem with this approach is that when the data sets are large and the networks are complicated, there is a serious growth in memory requirements because of data buffering and hence the performance suffers. A fine grained data flow environment has been proposed to alleviate this problem [12] ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization'92 Proceedings, pages 202--209, 1992.

....and simple model called clustered graphs, i.e. graphs with recursive clustering structures (see Fig. 1) This clustering structure can be used to model information in many areas, such as software engineering [23] knowledge representation [13] idea organization [14] software visualization [22] and VLSI design [10] Planarity is a much studied area for classical graphs. For example, the problem of minimizing edge crossings is proved to be NP hard [9, 7] However, efficient algorithms for testing whether a graph is planar (i.e. can be drawn without edge crossings) exist [11, 16, 3, 6] ....

C. Williams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Visualization 92, pages 202 -- 209, 1992.

....spatial and or temporal requirements as the solution algorithm progresses. Software systems such as AVS from Application Visualization Systems Inc. 20] Iris Explorer from Silicon Graphics, and Visualization Data Explorer from IBM [21] have made this archetype popular for scientific visualization [22]. Our work has extended this paradigm into the realm of scientific computation. As a simple example, consider a group of visualization modules within our dataflow network, illustrated in Figure 1. The boxes represent computational algorithms (modules) and the lines represent data pipes between ....

C. Willams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Proceedings of Visualization '92, pages 202--209. IEEE Press, 1992.

....development teams have designed and implemented interactive visualization environments. Giving an in depth analysis of other visualization environments is outside the scope of this paper. Instead, we discuss only some issues that resemble our blackboard architecture. Williams, Rasure and Hanson [13] provide a framework to understand design tradeoffs when developing data flow based visualization systems. Data flow systems are attractive because of the similarities with the visualization pipeline: users can easily organize their simulation, filter, mapping and render modules in an intuitive ....

C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In A.E. Kaufman and G.M. Nielson, editors, Proceedings Visualization '92, pages 202--209, 1992.

....of steering, and the vertical axis represents the method of interaction. The bubbles represent each of the systems predominant strength. Arrows indicate portions of the space that are also covered by the system. ware, Inc. These systems employ a dataflow based visual programming environment [131]. Due to their heritage [79] these systems have primarily been used in a visualization environment where the simulation is a single module or data are read from a file. As described above, they have also been used as a visualization engine for computational steering toolkits. The first three of ....

....have primarily been used in a visualization environment where the simulation is a single module or data are read from a file. As described above, they have also been used as a visualization engine for computational steering toolkits. The first three of these systems are compared in detail in [131]. Several important differences between these systems and SCIRun are discussed in Chapter 8. 2.2.1 AVS Advanced Visualization Systems (AVS) was the first company to market a dataflow system for scientific visualization. It uses a multiple process model, in which each module is implemented as a ....

WILLIAMS, C., RASURE, J., AND HANSEN, C. The state of the art of visual languages for visualization. In IEEE Visualization '92 (1992), pp. 202--209.

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C. Williams, J. Rasure, and C. Hansen. The State of the Art of Visual Languages for Visualization. In Proceedings Visualization '92, pages 202--209, 1992. 22

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C. Williams, J. Rasure, C. Hansen. The state of the art of visual languages for visualization. Visualization '92, Proc., IEEE Computer Society Press, 202--209, 1992

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C. Williams, J. Rasure, and C. Hansen, "The state of the art of visual languages for visualization," in Proceedings Visualization '92 -- sponsored by the IEEE Computer Society, pp. 202--209, 1992.

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C. Willams, J. Rasure, and C. Hansen. The state of the art of visual languages for visualization. In Proceedings of Visualization '92, pages 202--209. IEEE Press, 1992.

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