Navigating large networks with hierarchies

Eick, Stephen G.; Wills, Graham

doi:10.1109/visual.1993.398870

Cited by 62 publications

(38 citation statements)

References 4 publications

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“…This system is supposed to support users in visual exploration of linked information items. Ten years before Eick and Williams [11] already proposed a similar tool called HierNet for network-based visualization, which allows grouping, expanding and collapsing of information items. In Jigsaw [12] the authors present a system for investigative analysis across collections of text documents, exemplarily demonstrated on the ENRON data set.…”

Section: Tools For Data Explorationmentioning

confidence: 99%

Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

Gossen

Nitsche

Haun

et al. 2012

Bisociative Knowledge Discovery

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Abstract. In this chapter we explain the definition of the term (data) exploration. We refine this definition in the context of browsing, navigating and searching. We provide a definition of bisociative exploration and derive requirements on user interfaces, which are designed to support bisociative knowledge discovery. We discuss how to support subtasks of bisociative data exploration with appropriate user interface elements. We also present a set of exploratory tools, which are currently available or in development. Finally, we discuss the problem of usability evaluation in the context of exploratory search. Two main issues -complexity and comparability -are explained and possible solutions proposed.

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Section: Tools For Data Explorationmentioning

confidence: 99%

Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

Gossen

Nitsche

Haun

et al. 2012

Bisociative Knowledge Discovery

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show abstract

“…There are several approaches to visualize networks and data on these networks. Eick and Wills [9] use functions such as aggregation, hierarchical information, node position and linked displays for investigating large networks with hierarchies but without a natural layout. They used color and shape for coding node information and color and line width for coding link information.…”

Section: Line Phenomenamentioning

confidence: 99%

Visual Data Mining of Large Spatial Data Sets

Keim

Panse

Sips

2003

Databases in Networked Information Systems

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Abstract. Extraction of interesting knowledge from large spatial databases is an important task in the development of spatial database systems. Spatial data mining is the branch of data mining that deals with spatial (location) data. Analyzing the huge amount (usually terabytes) of spatial data obtained from large databases such as credit card payments, telephone calls, environmental records, census demographics etc. is, however, a very difficult task. Visual data mining applies human visual perception to the exploration of large data sets. Presenting data in an interactive, graphical form often fosters new insights, encouraging the formation and validation of new hypotheses to the end of better problem-solving and gaining deeper domain knowledge. In this paper we give a short overview of visual data mining techniques, especially the area of analyzing spatial data. We provide some examples for effective visualizations of spatial data in important application areas such as consumer analysis, e-mail traffic analysis, and census demographics.

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“…In the visualization community, a considerable number of advanced visualization techniques for multidimensional data have been proposed. Examples of visual data exploration approaches include geometric projection techniques such as parallel coordinates [28], [29], icon-based techniques (e.g., [51], [12]), hierarchical techniques (e.g., [46], [54], [56]), graph-based techniques (e.g., [21], [15]), pixel-oriented techniques (e.g., [31], [37], [39]), and combinations thereof ( [8], [7]). In general, the visualization techniques are used in conjunction with some interaction techniques (e.g., [17], [10], [3]) and, sometimes, also some distortion techniques [55], [45] (cf.…”

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confidence: 99%

Designing pixel-oriented visualization techniques: theory and applications

Keim¹

2000

IEEE Trans. Visual. Comput. Graphics

371

234

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AbstractÐVisualization techniques are of increasing importance in exploring and analyzing large amounts of multidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class of the pixel-oriented techniques. The basic idea of pixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel of the screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number of issues which are of high importance in developing pixel-oriented visualization techniques. The major goal of this article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions of well-defined optimization problems. This is true for the mapping of the data values to colors, the arrangement of pixels inside the subwindows, the shape of the subwindows, and the ordering of the dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets. The optimization functions for the mentioned design decisions are important for the effectiveness of the resulting visualizations. We show this by evaluating the optimization functions and comparing it the results to the visualization obtained in a number of different application.

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Navigating large networks with hierarchies

Cited by 62 publications

References 4 publications

Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

Data Exploration for Bisociative Knowledge Discovery: A Brief Overview of Tools and Evaluation Methods

Visual Data Mining of Large Spatial Data Sets

Designing pixel-oriented visualization techniques: theory and applications

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