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

Visual data mining techniques have proven to be of high value in exploratory data analysis and they also have a high potential for mining large databases. In this article, we describe and evaluate a new visualization-based approach to mining large databases. The basic idea of our visual data mining techniques is to represent as many data items 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. The major goal of this article is to evaluate our visual data mining techniques and to compare them to other well-known visualization techniques for multidimensional data: the parallel coordinate and stick figure visualization techniques. For the evaluation of visual data mining techniques, in the first place the perception of properties of the data counts, and only in the second place the CPU time and the number of secondary storage accesses are important. In addition to testing the visualization techniques using re...

Serial periodic data exhibit both serial and periodic properties. For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set. We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. We show several applications of the spiral visualization to data exploration tasks, present our implementation, discuss the capacity for data analysis, and present findings of our informal study with users in data-rich scientific domains. Keywords Information Visualization, Spiral, Data Visualization, Interactive Data Exploration, Serial Periodic Data. INTRODUCTION Information visualization is the practice of mapping data sets into visual media for...

In this paper, we provide an overview of several pixel-oriented visualization techniques which have been developed over the last years to support an effective querying and exploration of large databases. Pixel-oriented techniques use each pixel of the display to visualize one data value and therefore allow the visualization of the largest amount of data possible. The techniques may be divided into query-independent techniques which directly visualize the data (or a certain portion of it) and query-dependent techniques which visualize the relevance of the data with respect to a specific query. An example for the class of query-independent techniques is the recursive pattern technique which is based on a generic recursive scheme generalizing a wide range of pixel-oriented arrangements for visualizing large databases. Examples for the class of query-dependent techniques are the generalized spiral and circle-segments techniques, which visualize the distances with respect to a database quer...

An important goal of visualization technology is to support the exploration and analysis of very large amounts of data. In this paper, we describe a set of pixeloriented visualization techniques which use each pixel of the display to visualize one data value and therefore allow the visualization of the largest amount of data possible. Most of the techniques have been specifically designed for visualizing and querying large databases. The techniques may be divided into query-independent techniques which directly visualize the data (or a certain portion of it) and query-dependent techniques which visualize the data in the context of a specific query. Examples for the class of query-independent techniques are the screen-filling curve and recursive pattern techniques. The screen-filling curve techniques are based on the well-known Morton and Peano-Hilbert curve algorithms, and the recursive pattern technique is based on a generic recursive scheme which generalizes a wide range of pixel-ori...

this report is to look at interactions in Visualization User Analysis DataBase Fig. 1. Interactions between the user, Database, Visualization, and Ana lysis The components of scenarios in user interaction explored in the report database exploration, from a system and user perspective. The ultimate research goal is to effectively support user-data interactions in both the underlying data models and high-level data interfaces. The two primary focus areas are: 1. Interactions Between System Components - What are the system components, and how do they interact/communicate with each other? What are the duties and responsibilities of each component? What constraints must be considered to integrate the components? 2. Interactions Between User and System - What does the user interact with and how does the user interact with each system component? What additional constraints does the user impose on the system, or what constraints are placed on the user by the system? In this report we describe the system components and outline several research issues necessary to make the visualization component the interface to the analysis, database, and visualization. Figure 2 shows the user interacting directly with the visualization, which then acts as an intermediary to the other systems. This changes the user from being surrounded by many tools, to one using and interacting with a single tool, but managing the same sophistication of analysis, data-mining, knowledge discovery, and visualization. Visualization's effective human communication qualities enable it to play this important role.

Serial periodic data exhibit both serial and periodic properties. For example, time continues forward serially, but weeks, months, and years are periods that recur. While there are extensive visualization techniques for exploring serial data, and a few for exploring periodic data, no existing technique simultaneously displays serial and periodic attributes of a data set. We introduce a spiral visualization technique, which displays data along a spiral to highlight serial attributes along the spiral axis and periodic ones along the radii. We show several applications of the spiral visualization to data exploration tasks, present our implementation, discuss the capacity for data analysis, and present findings of our informal study with users in data-rich scientific domains. Keywords Information Visualization, Spiral, Data Visualization, Interactive Data Exploration, Serial Periodic Data. INTRODUCTION Information visualization is the practice of mapping data sets into visual media for...