Abstract. In today's applications data is produced at unprecedented rates. While the capacity to collect and store new data rapidly grows, the ability to analyze these data volumes increases at much lower rates. This gap leads to new challenges in the analysis process, since analysts, decision makers, engineers, or emergency response teams depend on information hidden in the data. The emerging field of visual analytics focuses on handling these massive, heterogenous, and dynamic volumes of information by integrating human judgement by means of visual representations and interaction techniques in the analysis process. Furthermore, it is the combination of related research areas including visualization, data mining, and statistics that turns visual analytics into a promising field of research. This paper aims at providing an overview of visual analytics, its scope and concepts, addresses the most important research challenges and presents use cases from a wide variety of application scenarios.

Konstanz University • Scale of Things to Come (information, drivers, kinds) • Today's interaction designed for point and click on individual Challenge of the Information Age Today s interaction designed for point and click on individual items, groups(folders), and lists • Today's interaction assumes user knows subject, concepts within information spaces, and can articulate what they want • Today's interaction assumes data and interconnecting relationships are static in meaning over time Japan Protection Measures Japan Trade Protection Trade Protection Measures Vis'07-Scope and Challenges of Visual Analytics-Keim / Thomas Trade Protection Measures Konstanz University • Changing Nature of Information Structure: Temporal, dynamically changing relationships, determination of intent (DC Sniper & ThemeRiver) Examples Demonstrating Need Vis'07-Scope and Challenges of Visual Analytics-Keim / Thomas Konstanz University Outline Konstanz University Visual Analytics Definition Visual Analytics is the science of analytical reasoning facilitated by interactive visual interfaces. People use visual analytics tools and techniques to Synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data. Detect the expected and discover the unexpected. Provide timely, defensible, and understandable assessments. Vis'07-Scope and Challenges of Visual Analytics-Keim / Thomas y, , Communicate assessment effectively for action. "The beginning of knowledge is the discovery of something we do not understand." ~Frank Herbert (1920-1986) Konstanz University Research Areas Related to Visual Analytics Vis'07-Scope and Challenges of Visual Analytics-Keim / Thomas

Visual exploration of multivariate data typically requires projection onto lower-dimensional representations. The number of possible representations grows rapidly with the number of dimensions, and manual exploration quickly becomes ineffective or even uoJeasible. This paper proposes automatic analysis methods to extract potentially relevant visual structures from a set of candidate visualizations. Based on features, the visualizations are ranked in accordance with a specified user task. The user is provided with a manageable number of potentially useful candidate visualizations, which can be u ed as a starting point for interactive data analysis. This can effectively t:ase the task of finding truly useful visualizations and potcntially speed up the data exploration task. In this paper, we present ranking measures for class-based as well as non class-based Scatterplots and Parallel Coordinates visualizations. The proposed analysis methods are evaluated on different datasets.

During the last two decades a wide variety of advanced methods for the Visual Exploration of large data sets have been proposed. For most of these techniques user interaction has become a crucial element, since there are many situations in which an user or an analyst has to select the right parameter settings from among many or select a subset of the available attribute space for the visualization process, in order to construct valuable visualizations that provide insight into the data and reveal interesting patterns. The right choice of input parameters is often essential, since suboptimal parameter settings or the investigation of irrelevant data dimensions make the exploration process more time consuming and may result in wrong conclusions. In this paper we propose a novel method for automatically determining meaningful parameter-and attribute settings based on the Information content of the resulting visualizations.Our technique called Pixnostics, in analogy to Scagnostics[1] automatically analyses pixel images resulting from diverse parameter mappings and ranks them according to the potential value for the user. This allows a more effective and more efficient visual data analysis process, since the attribute/parameter space is reduced to meaningful selections and thus the analyst obtains faster insight into the data. Real world applications are provided to show the benefit of the proposed approach.

Figure 1: Radial Traffic Analyzer is a visual tool for interactive packet-level analysis of data flows in a computer network. The technique is useful to compare network load in a geographically aware display, to relate communication partners, and to identify the types of network traffic occurring at the considered network hosts.