AbstractÐVisualization techniques are ofincreasing importance in exploring and analyzing large amounts ofmultidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class ofthe pixel-oriented techniques. The basic idea ofpixel-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 ofthe 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 ofissues which are ofhigh importance in developing pixel-oriented visualization techniques. The major goal ofthis 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 ofwell-defined optimization problems. This is true for the mapping ofthe data values to colors, the arrangement ofpixels inside the subwindows, the shape ofthe subwindows, and the ordering ofthe 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. Index TermsÐInformation visualization, visualizing large data sets, visualizing multidimensional and multivariate data, visual data exploration, visual data mining. 1

In this paper, we describe a novel technique for visualizing large amounts of high-dimensional data, called ‘circle segments’. The technique uses one colored pixel per data value and can therefore be classified as a pixel-per-value technique [Kei 96]. The basic idea of the ‘circle segments ’ visualization technique is to display the data dimensions as segments of a circle. If the data consists of k dimensions, the circle is partitioned into k segments, each representing one data dimension. Inside the segments, the data values belonging to one dimension are arranged from the center of the circle to the outside in a back and forth manner orthogonal to the line that halves the segment. Our first results show that the ‘circle segment’ technique is very powerful for visualizing large amounts of data, providing more expressive visualizations than other wellknown techniques such as the ‘recursive pattern ’ technique and traditional

Visual clutter denotes a disordered collection of graphical entities in information visualization. Clutter can obscure the structure present in the data. Even in a small dataset, clutter can make it hard for the viewer to find patterns, relationships and structure. In this paper, we define visual clutter as any aspect of the visualization that interferes with the viewer’s understanding of the data, and present the concept of clutter-based dimension reordering. Dimension order is an attribute that can significantly affect a visualization’s expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing information content or modifying the data in any way. Clutter reduction is a display-dependent task. In this paper, we follow a three-step procedure for four different visualization techniques. For each display technique, first, we determine what constitutes clutter in terms of display properties; then we design a metric to measure visual clutter in this display; finally we search for an order that minimizes the clutter in a display.

Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships

Tracking and especially comparing psychotherapeutic processes is a complex task involving a large number of individual and complexly related parameters. Therefore, descriptive and classical statistical methods are only suited for partial analyses. To overcome these limitations we introduce LinkVis, a new Information Visualization (InfoVis) tool used to visualize and evaluate psychotherapeutic processes. LinkVis is developed and clinically tested on the basis of Cognitive Behavioral Therapy treating anorectic girls. The user gets new insight into the data under investigation due to the combination of three di#erent visualization techniques: scatterplots, Cherno# faces, and parallel coordinates. LinkVis supports exploring of complex timedependent data in order to gain more information about the psychotherapeutic process, especially when comparing di#erent patients and groups.

Note: This paper is an expanded version of \MOTC: An Aid to Multidimensional

Large financial institutions such as Bank of America handle hundreds of thousands of wire transactions per day. Although most transactions are legitimate, these institutions have legal and financial obligations in discovering those that are suspicious. With the methods of fraudulent activities ever changing, searching on predefined patterns is often insufficient in detecting previously undiscovered methods. In this paper, we present a set of coordinated visualizations based on identifying specific keywords within the wire transactions. The different views used in our system depict relationships

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Due to complexity, modern visualization techniques for large data volumes and complex interrelationships are difficult to understand for non-expert users and even for expert users the visualization result may be difficult to interpret. Often the limited screen space and the risk of occlusion hinders a meaningful explanation of techniques or datasets by additional visual elements. This paper presents a novel way how views from information visualization can be adapted by the use of the well-known illustrative technique ”exploded view”, to successfully face the problems described above. The application of exploded views gains screen space for an explanation in a smart way and acts explanatory itself. With our approach of illustrating visual representations, the understanding of complex visualization techniques is eased and new comprehensible views on data are given. 1.

Few tools exist for data exploration and pattern identification in time series data sets. Timeboxes are rectangular, direct-manipulation queries for studying time-series datasets. Timeboxes are the primary query tool in our TimeSearcher application, which supports interactive exploration via dynamic queries, along with overviews of query results and drag-and-drop support for query-by-example. This paper describes the TimeSearcher application and possible extensions to the timebox query model, along with a discussion of the use of TimeSearcher for exploring a time series data set involving gene expression profiles.