Research
Explorative Data Management in Bioinformatics | |
| advisor | Prof. Dr. Michael Berthold |
| project description | Data analysis in bioinformatics requires new strategies to face the diversity and abundance of data. The existing data are often unreliable and not suitable for certain purposes. Models that help to extract information from unprecise data have to be found. The development of a platform to support the visual exploration and interpretation of the relevant information is central to the project. |
| suggested doctoral theses |
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Network Text Analysis and Visualization | |
| advisor | Prof. Dr. Ulrik Brandes |
| project description | The volume of electronically available text is increasing dramatically. Consequently, there is a growing demand for tools and methods to structure, filter, classify, browse or search large amounts of text. We are interested in exploring the use of network representations of text structure and text corpora, both for analysis and visual interaction. |
| suggested doctoral theses |
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Efficient Rendering of Large 3D Geometry Models | |
| advisor | Prof. Dr. Oliver Deussen |
| project description | The complexity and size of geometric models used in computer graphics and CAD is constantly increasing.Specific techniques for efficient storage, representation and manipulation of these data have to be found. |
| suggested doctoral theses |
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Visual Data Mining | |
| advisor | Prof. Dr. Daniel Keim |
| project description | The abundance of electronic information from different sources makes it more and more difficult for the user to extract the relevant information. Our projects aim to develop computer aided support for data exploration. We plan to combine automatic and interactive methods for data exploration with visualization techniques in order to provide new tools for an efficient analysis of large data sets. |
| planned projects: |
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| suggested doctoral theses |
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Visualization of Error Traces in Large State Spaces | |
| advisor | Prof. Dr. Stefan Leue |
| project description | The complexity of the software systems that we use in daily life increases rapidly. These systems exhibit tremendous complexity both in terms of the services that they provide and in their internal software architecture. For example, the size of the state spaces of these systems can be enormous and easily reach 225 and more. Automated formal analysis of these software system is essential in ensuring safe operation, in particular for embedded software systems. The aim of this project is to reconcile formal software analysis methods, such as model checking, and software visualization in order to enhance understanding of the dynamic behaviour that these system exhibit. |
| suggested doctoral theses |
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VisMeb - A Visual Metadata Browser for Visual Data and Text Mining | |
| advisor | Prof. Dr. Harald Reiterer |
| project description | The main object of research is the design of the Human-Computer Interaction (HCI) using a variety of new visualizations and interaction techniques to support the information retrieval process for huge databases, such as digital libraries, the internet, data warehouses, or product databases. Considering the status quo in HCI research, the challenge is first and foremost on providing completely new forms of HCI by developing interactive visual artifacts not yet available as well as new techniques of interaction. At this point the realm of traditional GUI design is left for completely new ways of visual interaction with the medium computer. One objective is finding suitable visual metaphors for abstract data; usually metadata of a certain object are visualized, as for instance specific attributes of a web document (e.g. title, date of creation, size, language, relevance). A similar challenge is the design of intuitive ways of interaction (e.g. by making use of the different sense modalities, such as visual perception, speech, gesture, touch) that can be employed with a variety of hardware (e.g. wall-sized displays, PCs, PDAs). Still another challenge has to do with so called mobile devices that are becoming more and more integrated in work routine and the resulting ubiquitous computing. One emphasis will be on extensively evaluating all research prototypes by methods of usability engineering. For this purpose several mock-ups and prototypes are to be designed and then evaluated concerning feasibility ("proof of concepts") as well as utility and usability. |
| suggested doctoral theses |
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Geometry-based 3D Signal Processing | |
| advisor | Prof. Dr. Dietmar Saupe |
| project description |
The goal of our projects is to develop and extend signal processing methods
for three dimensional scenarios, like filtering, resampling, compression,
transformation and visualization. A special project is the data acquisition, analysis, visualization and evaluation of performance parameters in race bike training. For a detailed project description refer to our Powerbike webpage. |
| suggested doctoral theses |
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Intelligent Management of very large Structured and Semi-Structured Data | |
| advisor | Prof. Dr. Marc Scholl |
| project description | Algorithms for querying or storing datasets reach their bounds when the amount of data is large or very large. Filesystems with OS support offer one single alternative for structuring the data: sequential aggregation of bytes. This solution does not seem applicable when hierarchies, relations and semantic relations between the data become important for efficient methods of data exploration. |
| suggested doctoral theses |
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Visual Search and Analysis Methods for Complex Data | |
| advisor | Jun.-Prof. Dr. Tobias Schreck |
| project description | Today in many domains, archives with valuable information are compiled, curated and preserved. Examples include Digital Libraries of data from scientific experimentation, or the social sciences. Multimedia archives compiled by public broadcast institutions are another example. Understanding about the contents of these archives, and finding of interesting content, are important tasks to make use of such data. In this project we want to develop new methods for retrieval and analysis in data repositories of complex and heterogeneous data. Key research questions are how combined queries for complex data including multiple different data types can be visually specified, and how respective results can be effectively visualized. |
| suggested doctoral theses |
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