Tobias Ruppert

• Dipl.-Math.
• Research Assistant at Fraunhofer Institute for Computer Graphics Research

The exploration of text document collections is a complex and cumbersome task. Clustering techniques can help to group documents based on their content for the generation of overviews. However, the underlying clustering workflows comprising preprocessing, feature selection, clustering algorithm selection and parameterization offer several degrees o...

The process of political decision making is often complex and tedious. The policy process consists of multiple steps, most of them are highly iterative. In addition, different stakeholder groups are involved in political decision making and contribute to the process. A series of textual documents accompanies the process. Examples are official docum...

The EU Community project seeks to promote, facilitate, and ultimately exploit the synergy of a cutting-edge intelligent collaboration platform with a community of institutional actors, stakeholders, scientists, consultants, media analysts and other individuals that can make valuable contributions to EU policy debates. Its ultimate goal is to effect...

Today’s politicians are confronted with new information technologies to tackle complex decision-making problems. In order to make sustainable decisions, a profound analysis of societal problems and possible solutions (policy options) needs to be performed. In this policy-analysis process, different stakeholders are involved. Besides internal direct...

The purpose of this ongoing work is to motivate public policy making as an application area for information visualization and visual analytics. Through our expertise gathered in several policy making-related projects, we identified parallels between the benefits of visualization and the needs of evidence-based public policy making. In the following...

Decision making is a complex process consisting of several consecutive steps. Before converting a decision into effective action the problem to be tackled needs to be analyzed, alternative solutions need to be developed, and the best solution needs to be picked. In many cases computational models support decision makers in this process. Therefore,...

To this day, data-driven science is a widely accepted concept in the digital library (DL) context (Hey et al. in The fourth paradigm: data-intensive scientific discovery. Microsoft Research, 2009). In the same way, domain knowledge from information visualization, visual analytics, and exploratory search has found its way into the DL workflow. This...

Decision making in the field of policy making is a complex task. On the one hand conflicting objectives influence the availability of alternative solutions for a given problem. On the other hand economic, social, and environmental impacts of the chosen solution have to be considered. In the political context, these solutions are called policy optio...

The definition of similarity between data objects plays a key role in many analytical systems. The process of similarity definition comprises several challenges as three main problems occur: different stakeholders, mixed data, and changing requirements. Firstly, in many applications the developers of the analytical system (data scientists) model th...

The complexity of actual decision making problems especially in the field of policy making is increasing due to conflicting aspects to be considered. Methods from the field of strategic environmental assessment consider environmental, economic, and social impacts caused by political decisions. This makes the analysis of reasonable decisions more co...

Today's politicians are confronted with new (digital) ways to tackle complex decision-making problems. In order to make the right decisions profound analysis of the problems and possible solutions has to be performed. Therefore policy analysts need to collaborate with external experts consulted as advisors. Due to different expertises of these stak...

Visual analysis of time series data is an important, yet challenging task with many application examples in fields such as financial or news stream data analysis. Many visual time series analysis approaches consider a global perspective on the time series. Fewer approaches consider visual analysis of local patterns in time series, and often rely on...

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clusterin...

Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clusterin...

Nowadays daily office work consists of dealing with big numbers of data and data sources, and furthermore of working with complex computer programs. In consequence many users have problems to use such programs effective and efficient. In particular beginners have significant problems to use the programs correctly due to complex functionality and in...

This article looks at the current and future roles of information visualization, semantics visualization, and visual analytics in policy modeling. Many experts believe that you can't overestimate visualization's role in this respect.

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented dat...

Today's digital libraries (DLs) archive vast amounts of information in the form of text, videos, images, data measurements, etc. User access to DL content can rely on similarity between metadata elements, or similarity between the data itself (content-based similarity). We consider the problem of exploratory search in large DLs of time-oriented dat...

We present approaches for visualizing uncertainty in an application context through techniques for the visualization of uncertainty. We also describe methods for the reduction of the complexity of the visualization to avoid cognitive overload. Uncertainty in both natural and man-made structures under ground is thus communicated to the user in an ap...

We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usua...

Cities are under constant development. They are characterized not only by their surface constructions like buildings and traffic infrastructure, but also by their underground structures. Besides human-created lifelines, tunnels and quarries, there are also diverse geological formations. Underground information contains a lot of uncertainty by natur...

The visual analysis of video content is an important research topic due to the huge amount of video data that is generated every day. Annotating this data will become a major problem since the amount of videos further increases. With this work we introduce a system that combines a visualization tool with automatic video segmentation techniques and...

Digital Library support for textual and certain types of non-textual documents has significantly advanced over the last years. While Digital Library support implies many aspects along the whole library workflow model, interactive and visual retrieval allowing effective query formulation and result presentation are important functions. Recently, new...

Wenn Wissenschaftler Daten (z.B. des Studentenpisa-Tests des SPIEGEL) analysieren, stellen sie gemeinhin Hypothesen auf und überprüfen diese dann. In diesem Beitrag wird ein anderes Verfahren vorgestellt. Es handelt sich um ein exploratives Vorgehen, das es erlaubt, versteckte Zusammenhänge in großen und komplexen Datensammlungen zu finden. Dazu we...

Modern cities are under constant development. They are characterized not only by their surface constructions like buildings and traffic infrastructure, but also by their underground structures. Besides human-created lifelines, tunnels and quarries, there are also diverse geological formations. All this information is important for a sustainable urb...

Professionals involved in governance and policy modelling have seen a dramatic increase in the volume of potentially relevant data. As in many other knowledge-based fields today, policy makers face the problem of an information overload. Putting data from sources as diverse as blogs, online opinion polls and government reports to effective use is a...