From visual data exploration to visual data mining: a survey <s> Taxonomy of Visualization Systems <s> A useful starting point for designing advanced graphical user interfaces is the visual information seeking Mantra: overview first, zoom and filter, then details on demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. The paper offers a task by data type taxonomy with seven data types (one, two, three dimensional data, temporal and multi dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extracts). <s> BIB001 </s> From visual data exploration to visual data mining: a survey <s> Taxonomy of Visualization Systems <s> A programmable DC power supply including a proportionally controlled transistor connected in series with the load for varying the load current in accordance with the magnitude of a control signal applied to the transistor. A detector coupled across the transistor functions to sense high and low voltage thresholds and thereupon select an incrementally higher or lower voltage for application to the load to assure that the transistor operates continuously in its proportional range, thereby enabling the input signal to maintain control of the load current. <s> BIB002 </s> From visual data exploration to visual data mining: a survey <s> Taxonomy of Visualization Systems <s> 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. <s> BIB003
by Card et al. Card et al. BIB002 adopt an alternative approach toward a categorization of information visualizations, grouping their application into four different levels. At the highest level are visualization tools that provide users with visual access to information collections external to their immediate environment, such as the Internet or online databases on a server. At the second level are tools aimed at supporting people in executing tasks by creating fast accessible and highly interactive visual representations of the information workspace required by the task. These are the Workspace tools, targeted at augmenting users capabilities of interacting with their information workspace. At the third level are the Visual Knowledge Tools that depict visual representations of some data and a set of controls for interacting with such representations so that users can determine and extract relationships from the data. This category encompasses most of the tools targeted at producing visualizations of Data Tables. Finally, at the fourth level are visually enhanced objects whose focus is on revealing more information about an object of intrinsic visual form. A good example is a medical visualization of a human organ using direct volume visualization to depict internal structures. Visual Knowledge Tools are further categorized based on the type of Visual Structures (VS) they adopt. The concept of Visual Structures embeds how space is used to encode information or, in other words, the dimensionality of the data representations used. To some extent, the VS adopted also reflects the task that the environment is meant to support. Common types of Visual Structures are: . Physical, referring mainly to data representations that have a direct correspondence to "real world" objects, typical of Scientific Visualization. They comprise techniques for constructing and viewing 3D representations of real world objects such as the human body, buildings, or molecules for the purpose of extracting information. . 1D, 2D, 3D, referring to visualizations that encode information by positioning marks on orthogonal axes. 1D VSs are typically used for timelines and text documents, usually as part of a larger VS. They also lend themselves to being used as controls, such as sliders and scroll bars indicating the range of values of a certain parameter. Examples of 2D VSs are 2D scatter graphs and scatter graph matrices. 3D VSs are common for physical data, but are also used for composing 2D visualizations and for 3D abstract representations. . Multi-d information visualization environments handle abstract data with too many attributes to be encoded directly in the 1D, 2D, or 3D VSs. Usually, the multiple attributes are not primarily of a spatial nature and have no explicit structure or relations. Scientific visualization also deals with multi-d data, but most of the scientific data sets have spatial attributes that are determinant for creating visualizations. Typical tasks that must be supported by such environments involve getting knowledge from the data, like finding patterns, relationships, clusters, gaps, and outliers, or finding specific items using interaction actions, such as zooming, filtering, and selection. . Tree and Network denote VSs that use connection and enclosure to encode relationships among data items. These correspond, to an extent, to the hierarchical and graph-based groups of techniques in Keim's classification. Hierarchies naturally arise when describing, for example, taxonomies, organization structures, and disk space management information. Visualization techniques targeted at this domain attempt to simultaneously show many nodes, if not the entire tree itself, while providing mechanisms for navigation and searching that allow users to retain the overall tree structure and reduce disorientation. Hierarchies are similar to multi-d data in the sense that their nodes usually contain a fair number of attributes. Network VSs often describe data consisting of nodes representing data points and links representing a relationship between two data points, plus additional information associated with data items or . Pixel-based visualizations using the spiral (left) and axes (right) query-dependent arrangements of pixels, from a synthetic data set with 7,000 data items and eight attributes. Color coding ranges from yellow for those data items that better satisfy a posed query to green, blue, red, and almost black for those further away from it. From Keim and Kriegel  . Fig. 6 . Arrangement of data items in the Circle Segments pixel-based technique. The circle is divided into n segments for an n-dimensional data set (in the figure, n = 8) and each data attribute (dimension) is shown within a segment. From BIB003 . connections. Much work has already been done in this field; however, the sheer complexity of relationships and user tasks, particularly for large networks, still leaves much to be done. Typical application areas are network and traffic management, digital libraries, and visualization of World Wide Web structure. Card et al. argue that a classification based on VSs is better suited for categorizing dynamic interactive environments than one based on data dimensionality. If an environment is classified as using a 2D VS, then it can potentially handle any data that can be mapped into such a VS. Such a classification builds on an original taxonomy of information visualization environments by Shneiderman BIB001 and North 1 and first appeared in OLIVE-The Online Library of Information Visualization Environments, 2 a useful resource with a good coverage of techniques and systems up to 1997.