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.

The importance of the Internet and our dependency on computer networks are steadily growing, which results in high costs and substantial consequences in case of successful intrusions, stolen data, and interrupted services. At the same time, a trend towards massive attacks against the network infrastructure is noticeable. Therefore, monitoring large networks has become an important field in practice and research. Through monitoring systems, attacks can be detected and analyzed to gain knowledge of how to better protect the network in the future. In the scope of this paper, we present a system to analyze NetFlow data using a relational database system. NetFlow records are linked with alerts from an intrusion detection system to enable efficient exploration of suspicious activity within the monitored network. Within the system, the monitored network is mapped to a TreeMap visualization, the attackers are arranged at the borders and linked using splines parameterized with prefix information. In a series of case studies, we demonstrate how the tool can be used to judge the relevance of alerts, to reveal massive distributed attacks, and to analyze service usage within a network.

Spatiotemporal analysis of sensor logs is a challenging research field due to three facts: a) traditional two-dimensional maps do not support multiple events to occur at the same spatial location, b) three-dimensional solutions introduce ambiguity and are hard to navigate, and c) map distortions to solve the overlap problem are unfamiliar to most users. This paper introduces a novel approach to represent spatial data changing over time by plotting a number of non-overlapping pixels, close to the sensor positions in a map. Thereby, we encode the amount of time that a subject spent at a particular sensor to the number of plotted pixels. Color is used in a twofold manner; while distinct colors distinguish between sensor nodes in different regions, the colors' intensity is used as an indicator to the temporal property of the subjects' activity. The resulting visualization technique, called Growth Ring Maps, enables users to find similarities and extract patterns of interest in spatiotemporal data by using humans' perceptual abilities. We demonstrate the newly introduced technique on a dataset that shows the behavior of healthy and Alzheimer transgenic, male and female mice. We motivate the new technique by showing that the temporal analysis based on hierarchical clustering and the spatial analysis based on transition matrices only reveal limited results. Results and findings are cross-validated using multidimensional scaling. While the focus of this paper is to apply our visualization for monitoring animal behavior, the technique is also applicable for analyzing data, such as packet tracing, geographic monitoring of sales development, or mobile phone capacity planning.

The term Visual Analytics has been around for almost five years by now, but still there are ongoing discussions about what it actually is and in particular what is new about it. The core of our view on Visual Analytics is the new enabling and accessible analytic reasoning interactions supported by the combination of automated and visual analysis. In this paper, we outline the scope of Visual Analytics using two problem and three methodological classes in order to work out the need for and purpose of Visual Analytics. By examples of analytic reasoning interaction, the respective advantages and disadvantages of automated and visual analysis methods are explained leading to a glimpse into the future of how Visual Analytics methods will enable us to go beyond what is possible when separately using the two methods.