Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

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Information visualization and visual data mining

We survey work on the different uses of graphical mapping and interaction techniques for visual data mining of large data sets represented as table data. Basic terminology related to data mining, data sets, and visualization is introduced. Previous work on information visualization is reviewed in light of different categorizations of techniques and systems. The role of interaction techniques is discussed, in addition to work addressing the question of selecting and evaluating visualization techniques. We review some representative work on the use of information visualization techniques in the context of mining data. This includes both visual data exploration and visually expressing the outcome of specific mining algorithms. We also review recent innovative approaches that attempt to integrate visualization into the DM/KDD process, using it to enhance user interaction and comprehension.

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From visual data exploration to visual data mining: a survey

Much of the attention in visualization research has focussed on data rooted in physical phenomena, which is generally limited to three or four dimensions. However, many sources of data do not share this dimensional restriction. A critical problem in the analysis of such data is providing researchers with tools to gain insights into characteristics of the data, such as anomalies and patterns. Several visualization methods have been developed to address this problem, and each has its strengths and weaknesses. This paper describes a system named XmdvTool which integrates several of the most common methods for projecting multivariate data onto a two-dimensional screen. This integration allows users to explore their data in a variety of formats with ease. A view enhancement mechanism called an N-dimensional brush is also described. The brush allows users to gain insights into spatial relationships over N dimensions by highlighting data which falls within a user-specified subspace. >

XmdvTool: integrating multiple methods for visualizing multivariate data

Visualization 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 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 important 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.

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Designing pixel-oriented visualization techniques: theory and applications

Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.

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Rolling the Dice: Multidimensional Visual Exploration using Scatterplot Matrix Navigation

The authors present a tool for the display and analysis of N-dimensional data based on a technique called dimensional stacking. This technique is described. The primary goal is to create a tool that enables the user to project data of arbitrary dimensions onto a two-dimensional image. Of equal importance is the ability to control the viewing parameters, so that one can interactively adjust what ranges of values each dimension takes and the form in which the dimensions are displayed. This will allow an intuitive feel for the data to be developed as the database is explored. The system uses dimensional stacking, to collapse and N-dimension space down into a 2-D space and then render the values contained therein. Each value can then be represented as a pixel or rectangular region on a 2-D screen whose intensity corresponds to the data value at that point. >

Exploring N-dimensional databases

Visual examination is widely used for evaluating the extent and severity of pavement distress. The visually assessed pavement surface condition is combined with structural information to rate the pavement on the basis of scoring systems that have been developed by the state transportation agencies to evaluate which pavement sections require regular maintenance, overlay, or complete reconstruction. Although scoring is often computerized, the raw data are usually collected by slow, laborious manual and visual methods during site inspections by trained field personnel and they are input into the computer manually. These methods are expensive and prone to subjectivity, error, and nonrepeatability. Automated surface distress evaluation has not developed rapidly because the accuracy of automated systems has not been sufficient to inspire confidence among the pavement engineers who have to rely on the evaluation results. System accuracy can be enhanced by using better engineering methods and data in designing the image acquisition and image processing portions of automated inspection systems. The visual signal--the apparent luminance of cracks in portland cement concrete (PCC) pavements and the contrast that they exhibit compared to surrounding pavement surfaces--is the input to the automated inspection system. The luminance depends on the reflectivities of the paving materials. Reflectivity measurement methods are specified and data are tabulated. In a companion paper in this Record computer modeling methods for detrmining and analyzing crack luminance are reported. The methods and data presented in these papers are useful for designing automated pavement inspection systems.

Visual appearance of surface distress in pcc pavements: i. crack luminance

Solid state (CCD, CID, or multiplexed photosensor) television cameras are the most widely used input devices in machine vision, because they are relatively inexpensive, rugged, and reliable. However, the design, specification, and testing of these cameras typically are geared to their primary use in producing images that will ultimately be observed by humans; the intended applications for these cameras are as diverse as parking lot security and home entertainment. Because the video information produced by the camera is not used in the same ways by people and machine vision systems, there is no a priori reason to expect that a camera designed for one use will be optimal for another. In our work we have examined what makes a camera suitable for machine vision use. This paper describes which characteristics are important to the camera's performance machine vision applications and why. We show how these characteristics can be measured and standardized using simple tests suitable for production screening or more extensive tests suitable for use in the laboratory. Tests for important camera characteristics, including transfer function, noise, and resolution, are described and test results for representative solid state cameras are presented. Finally, we discuss how such measurements can be useful in designing or selecting the components of a machine vision system: the video capture systems, the cameras, and the image processing algorithms.

How to select cameras for machine vision

A method is presented for placing line, point, and ring light sources to produce uniform illumination of planar surfaces. Optimization consists in setting to zero as many terms as possible in a two-dimensional Taylor series expansion of the surface illumination. We analyze several practical lighting arrangements and discuss the significance of symmetry in lighting design. Four conditions are sufficient to produce optimal illumination: (1) place all lamps in a single plane parallel to the illuminated surface; (2) arrange linear sources in parallel pairs; (3) arrange point-source lamps to achieve fourfold symmetry with respect to any two orthogonal axes lying in the surface, this requires a minimum of four lamps; and (4) select lamp heights so that a line connecting each lamp to the center of the illuminated surface forms specific angles with the surface normal: 30 deg for line sources and approximately 39 deg for point and ring sources. Any lamp arrangement meeting these conditions produces an illumination function in which at least three orders of terms of the Taylor series expansion are zero. We also discuss circumstances under which some of these conditions can be relaxed.

Uniform frontal illumination of planar surfaces: where to place the lamps

Automated pavement surface distress evaluation would be a useful component of computerized pavement management systems. Methods are described for applying computer simulations of pavement distress appearance to the design of those evaluation systems. By generalizing the results of crack visibility calculations under varying conditions of lighting, a number of conclusions about automated evaluation systems can be drawn, including the fact that there will always be some distress that escapes detection but that the detection probability is calculable. Lighting is important in determining which types of distress and what fraction of distress will evade detection. Differences between controlled and natural lighting, between ambient or omnidirectional lighting and directed lighting, and between single versus multiple light sources are discussed. Finally, there is consideration of how detailed examination of modeling results can be used to optimize the design of lighting, data acquisition hardware, and image-processing software. Because the conclusions depend strongly on the assumptions made in the work, a detailed listing of the assumptions is presented along with suggestions for further testing.

Norman Wittels

Papers

Exploring N-dimensional databases

Visual appearance of surface distress in pcc pavements: i. crack luminance

How to select cameras for machine vision

Uniform frontal illumination of planar surfaces: where to place the lamps

Lighting design for automated pavement surface distress evaluation