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.

Visualizations of large multi-dimensional data sets, occurring in scientific and commercial applications, often reveal interesting local patterns. Analysts want to identify the causes and impacts of these interesting areas, and they also want to search for similar patterns occurring elsewhere in the data set. In this paper we introduce the Intelligent Visual Analytics Query (IVQuery) concept that combines visual interaction with automated analytical methods to support analysts in discovering the special properties and relations of identified patterns. The idea of IVQuery is to interactively select focus areas in the visualization. Then, according to the characteristics of the selected areas, such as the data dimensions and records, IVQuery employs analytical methods to identify the relationships to other portions of the data set. Finally, IVQuery generates visual representations for analysts to view and refine the results. IVQuery has been applied successfully to different real-world data sets, such as data warehouse performance, product sales, and sever performance analysis, and demonstrates the benefits of this technique over traditional filtering and zooming techniques. The visual analytics query technique can be used with many different types of visual representation. In this paper we show how to use IVQuery with parallel coordinates, visual maps, and scatter plots.

Business operations involve many factors and relationships and are modeled as complex business process workflows. The execution of these business processes generates vast volumes of complex data. The operational data are instances of the process flow, taking different paths through the process. The goal is to use the complex information to analyze and improve operations and to optimize the process flow. In this paper, we introduce a new visualization technique, called VisImpact that turns raw operational business data into valuable information. VisImpact reduces data complexity by analyzing operational data and abstracting the most critical factors, called impact factors, which influence business operations. The analysis may identify single nodes of the business flow graph as important factors but it may also determine aggregations of nodes to be important. Moreover, the analysis may find that single nodes have certain data values associated with them which have an influence on some business metrics or resource usage parameters. The impact factors are presented as nodes in a symmetric circular graph, providing insight into core business operations and relationships. A cause-effect mechanism is built in to determine 'good' and 'bad' operational behavior and to take action accordingly. We have applied VisImpact to real-world applications, fraud analysis and service contract analysis, to show the power of VisImpact for finding relationships among the most important impact factors and for immediate identification of anomalies. The VisImpact system provides a highly interactive interface including drilldown capabilities down to transaction levels to allow multilevel views of business dynamics.

Cartograms are a well-known technique for showing geography-related statistical information, such as population demographics and epidemiological data. The basic idea is to distort a map by resizing its regions according to a statistical parameter, but in a way that keeps the map recognizable. In this paper, we deal with the problem of making continuous cartograms that strictly retain the topology of the input mesh. We compare two algorithms that solve the continuous cartogram problem. The first one uses an iterative relocation of vertices based on scanlines. This algorithm explicitly accounts for induced shape error. The second one is based on the Gridfit technique, which uses pixel-based distortion based on a quadtree-like data structure. The basic idea is to insert pixels, the number of which corresponds to a statistical parameter, into the data structure and distort the pixels such that every pixel obtains a unique, nonoverlapping position. Relocation of vertices of the map are positioned using the same distortion. We discuss the results obtained from both methods, compare their shape and area trade-offs as well as their efficiency, and show results from different applications.