Hartmut Ziegler

Visual Analytics: Scope and Challenges

In todays 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.

Visual Analytics: Combining Automated Discovery with Interactive Visualizations

In numerous application areas fast growing data sets develop with ever higher complexity and dynamics. A central challenge is to filter the substantial information and to communicate it to humans in an appropriate way. Approaches, which work either on a purely analytical or on a purely visual level, do not sufficiently help due to the dynamics and complexity of the underlying processes or due to a situation with intelligent opponents. Only a combination of data analysis and visualization techniques make an effective access to the otherwise unmanageably complex data sets possible. Visual analysis techniques extend the perceptual and cognitive abilities of humans with automatic data analysis techniques, and help to gain insights for optimizing and steering complicated processes. In the paper, we introduce the basic idea of Visual Analytics, explain how automated discovery and visual analysis methods can be combined, discuss the main challenges of Visual Analytics, and show that combining automatic and visual analysis is the only chance to capture the complex, changing characteristics of the data. To further explain the Visual Analytics process, we provide examples from the area of document analysis.

Visual market sector analysis for financial time series data

The massive amount of financial time series data that originates from the stock market generates large amounts of complex data of high interest. However, adequate solutions that can effectively handle the information in order to gain insight and to understand the market mechanisms are rare. In this paper, we present two techniques and applications that enable the user to interactively analyze large amounts of time series data in real-time in order to get insight into the development of assets, market sectors, countries, and the financial market as a whole. The first technique allows users to quickly analyze combinations of single assets, market sectors as well as countries, compare them to each other, and to visually discover the periods of time where market sectors and countries get into turbulence. The second application clusters a selection of large amounts of financial time series data according to their similarity, and analyzes the distribution of the assets among market sectors. This allows users to identify the characteristic graphs which are representative for the development of a particular market sector, and also to identify the assets which behave considerably differently compared to other assets in the same sector. Both applications allow the user to perform investigative exploration techniques and interactive visual analysis in real-time.

Visual Analytics on the Financial Market: Pixel-based Analysis and Comparison of Long-Term Investments

In this paper, we describe solutions how pixel-based visualization techniques can support the decision making process for investors on the financial market. We especially focus on explorative interactive techniques where analysts try to analyze large amounts of financial data for long-term investments, and show how visualization can effectively support an investor to gain insight into large amounts of financial time series data. After presenting methods for improving the traditional performance/risk computation in order to take user-specific regions of interest into account, we present a novel visualization approach that demonstrates how changes in these regions of interest affect the ranking of assets in a long-term investment strategy.

Advanced visual analytics interfaces

Advanced visual interfaces, like the ones found in information visualization, intend to offer a view on abstract data spaces to enable users to make sense of them. By mapping data to visual representations and providing interactive tools to explore and navigate, it is possible to get an understanding of the data and possibly discover new knowledge. With the advent of modern data collection and analysis technologies, the direct visualization of data starts to show its limitations due to limited scalability in terms of volumes and to the complexity of required analytical reasoning. Many analytical problems we encounter today require approaches that go beyond pure analytics or pure visualization. Visual analytics provides an answer to this problems by advocating a tight integration between automatic computation and interactive visualization, proposing a more holistic approach. In this paper, we argue for Advanced Visual Analytics Interfaces (AVAIs), visual interfaces in which neither the analytics nor the visualization needs to be advanced in itself but where the synergy between automation and visualization is in fact advanced. We offer a detailed argumentation around the needs and challenges of AVAIs and provide several examples of this type of interfaces.

COPERNICUS: context-preserving engine for route navigation with interactive user-modifiable scaling

In this paper, we present an automated system for generating context‐preserving route maps that depict navigation routes as a path between nodes and edges inside a topographic network. Our application identifies relevant context information to support navigation and orientation, and generates customizable route maps according to design principles that communicate all relevant context information clearly visible on one single page. Interactive scaling allows seamless transition between the original undistorted map and our new map design, and supports user‐specified scaling of regions of interest to create personalized driving directions according to the drivers needs.