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

Spatio-temporal c1ustering is a process of grouping objects based on their spatial and temporal similarity. It is relatively new subfield of data mining which gained high popularity especially in geographie information scienees due to the pervasiveness of all kinds of location-based or environmental devices that record position, time or/and environmental properties of an object or set of objects in real-time. As a consequence, different types and large amounts of spatio-temporal data became available that introduce new challenges to data analysis and require novel approaches to knowledge discovery. In this chapter we concentrate on the spatio-temporal c1ustering in geographic space. First, we provide a c1assification of different types of spatio-temporal data. Then, we focus on one type of spatio-temporal c1ustering -trajectory c1ustering, provide an overview of the state-of-the-art approach es and methods of spatio-temporal c1ustering and finally present several scenarios in different application domains such as movement, ceIIular networks and environmental studies.

We present the results of a controlled experiment to investigate the performance of different temporal glyph designs in a small multiple setting. Analyzing many time series at once is a common yet difficult task in many domains, for example in network monitoring. Several visualization techniques have, thus, been proposed in the literature. Among these, iconic displays or glyphs are an appropriate choice because of their expressiveness and effective use of screen space. Through a controlled experiment, we compare the performance of four glyphs that use different combinations of visual variables to encode two properties of temporal data: a) the position of a data point in time and b) the quantitative value of this data point. Our results show that depending on tasks and data density, the chosen glyphs performed differently. Line Glyphs are generally a good choice for peak and trend detection tasks but radial encodings are more effective for reading values at specific temporal locations. From our qualitative analysis we also contribute implications for designing temporal glyphs for small multiple settings.

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.