Keim, Daniel A.

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Abstract: The Internet has become a wild place: malicious code is spread on personal computers across the world, deploying botnets ready to attack the network infrastructure. The vast number of security incidents and other anomalies overwhelms attempts at manual analysis, especially when monitoring service provider backbone links. We present an approach to interactive visualization with a case study indicating that interactive visualization can be applied to gain more insight into these large data sets. We superimpose a hierarchy on IP address space, and study the suitability of Treemap variants for each hierarchy level. Because viewing the whole IP hierarchy at once is not practical for most tasks, we evaluate layout stability when eliding large parts of the hierarchy, while maintaining the visibility and ordering of the data of interest.

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Abstract: 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, the perception of data properties counts most, while the CPU time and the number of secondary storage accesses are only of secondary importance. In addition to testing the visualization techniques using real data, we developed a testing environment for database visualizations similar to the benchmark approach used for comparing the performance of database systems. The testing environment allows the generation of test data sets with predefined data characteristics which are important for comparing the perceptual abilities of visual data mining techniques.

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Abstract: 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.

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