Exploration und Visualisierung großer Informationsmengen
Im Zentrum des Forschungsprogramms des Graduiertenkollegs steht die Entwicklung von Methoden insbesondere im Rahmen von Visualisierung und Computergraphik zur Unterstützung von Exploration, Analyse und Management großer Datenräume. Dabei können diese Datenräume auch selbst visueller Natur sein, z.B. in Form von Multimediadokumenten oder komplexen geometrischen Strukturen. Die im Graduiertenkolleg besonders relevanten Fachrichtungen sind Informationsvisualisierung, Computergraphik, Human Computer Interaction, Intelligente Datenanalyse, Information Retrieval, Datenbanken und Informationssysteme, sowie digitale Kommunikation. Ein Ziel der Datenexploration und ¿analyse ist es, neue a-priori unbekannte, aber für den Anwender nützliche Informationen zu finden. Die Forschung zielt darauf ab, existierende Verfahren effektiver und effizienter zu machen sowie neue Verfahren der Exploration und Analyse zu entwickeln, die den speziellen Erfordernissen der z.B. im Internet gespeicherten und zu übertragenden Informationen gerecht werden. Dabei sollen die Informationen analysiert und gruppiert (geclustert) sowie ihre Qualität bewertet werden. Für diesen Prozess werden zunächst Informations-Repräsentations-Methoden der Informationswissenschaft sowie Datenmodellierungsmethoden aus dem Bereich Datenbanksysteme benötigt. Die Informationsmodellierung ist dann die Ausgangsbasis für die eigentliche Exploration der Daten, die durch eine Kombination von automatischen Clusteranalyseverfahren, einer wissensbasierten semantischen Analyse, sowie einer interaktiven Visualisierung der Daten erfolgen soll. Ein gewichtiger Anwendungsbereich der im Graduiertenkolleg zu entwickelnden Verfahren liegt in der <I> explorativen Analyse von großen Beständen an Bioinformatikdaten </i> und bildet eine Klammer um die meisten der in dem Graduiertenkolleg angestrebten Forschungsprojekte. Das Kolleg implementiert neue Betreuungs- und Ausbildungsstrukturen für Doktoranden. In der ersten Ausbildungsphase von zwei Semestern werden regelmäßig stattfindende Spezialvorlesungen gehalten und die Stipendiaten in Arbeitsgruppen, Praktika und Seminaren in die Forschungsaufgaben eingeführt. In dem folgenden Hauptteil der wissenschaftlichen Tätigkeit übernehmen Doktoranden und Postdoktoranden auch Forschungsorganisationsaufgaben bei der Planung und Durchführung von Workshops und internationalen Sommerschulen. Auslandsforschungsaufenthalte sind für alle Stipendiaten obligatorisch und runden das internationale Profil des Kollegs ab.
Central to the research programs of the Graduate College is the development of methods, especially within the framework of visualization and computer graphics, in support of data mining, data analysis, and the management of large information spaces, whereby the information spaces themselves may be visual in nature, i.e. in form of multimedia documents or complex geometric structures. The subject areas most relevant within the program are information visualization, computer graphics, human computer interaction, intelligent data analysis, information retrieval, database and information systems, as well as digital communication. One of the objectives of data mining and data analysis is to find new, previously unknown, yet useful information. The research aims at perfecting existing procedures to be more effective and more efficient, and at the same time it seeks to develop new procedures with regards to exploration and analysis, which serve more adequately special requirements, such as the vast information stored and transferred in the internet. The information must first be analyzed and clustered, as well as qualified. To complete this process, methods of knowledge representation within the area of information science, as well as data modeling methods within the area of database systems are needed. Hence, the information modeling is the starting point for the actual exploration of the data. The latter should be worked through using a combination of automatic cluster analysis procedures, knowledge based semantic analysis, and an interactive visualization of the data. An important range of application lies within the explorative analysis of vast amount of bioinformation data, and this commitment embraces most of the goals and objectives aimed at by the college. The graduate college will implement new supporting and educational structures for doctoral students. In the first phase, which will span two semesters, special lectures will be held on a regular basis. Students will be introduced to the scientific research in work groups, through practical applications, and seminars. In the second phase, which is also the core of the scientific activity, graduate students and post graduate students will be responsible for planning and implementing of workshops, as well as structuring and conduction international summer schools in addition to their own research projects. Research abroad is obligatory for all students and will round off the international profile of the graduate college.
- FB Informatik und Informationswissenschaft
(2016): Modeling, Simulation, and Optimization of Pacing Strategies for Road Cycling on Realistic Tracks |
In this study, we develop methods to model and simulate road cycling on real-world courses, to analyze the performance of individual athletes and to identify and quantify potential performance improvement. The target is to instruct the athlete where and how to optimize his pacing strategy during a time trial. Forschungszusammenhang (Projekte) |
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(2012): Building a Data Warehouse for Twitter Stream Exploration 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. IEEE, 2012, pp. 1341-1348. ISBN 978-1-4673-2497-7. Available under: doi: 10.1109/ASONAM.2012.230 |
In the recent year Twitter has evolved into an extremely popular social network and has revolutionized the ways of interacting and exchanging information on the Internet. By making its public stream available through a set of APIs Twitter has triggered a wave of research initiatives aimed at analysis and knowledge discovery from the data about its users and their messaging activities. While most of the projects and tools are tailored towards solving specific tasks, we pursue a goal of providing an application in dependent and universal analytical platform for supporting any kind of analysis and knowledge discovery. We employ the well established data warehousing technology with its underlying multidimensional data model, ETL routine for loading and consolidating data from different sources, OLAP functionality for exploring the data and data mining tools for more sophisticated analysis. In this work we describe the process of transforming the original stream into a set of related multidimensional cubes and demonstrate how the resulting data warehouse can be used for solving a variety of analytical tasks. We expect our proposed approach to be applicable for analyzing the data of other social networks as well. Forschungszusammenhang (Projekte) |
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(2012): Discovering Dynamic Classification Hierarchies in OLAP Dimensions CHEN, Li, ed., Alexander FELFERNIG, ed., Jiming LIU, ed., Zbigniew W. RAŚ, ed.. Foundations of Intelligent Systems. Berlin: Springer, 2012, pp. 425-434. ISBN 978-3-642-34623-1. Available under: doi: 10.1007/978-3-642-34624-8_48 |
The standard approach to OLAP requires measures and dimensions of a cube to be known at the design stage. Besides, dimensions are required to be non-volatile, balanced and normalized. These constraints appear too rigid for many data sets, especially semi-structured ones, such as user-generated content in social networks and other web applications. We enrich the multidimensional analysis of such data via content-driven discovery of dimensions and classification hierarchies. Discovered elements are dynamic by nature and evolve along with the underlying data set. Forschungszusammenhang (Projekte) |
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(2012): Organizing search results with a reference map IEEE Transactions on Visualization and Computer Graphics. 2012, 18(12), pp. 2546-2555. ISSN 1077-2626. eISSN 1941-0506. Available under: doi: 10.1109/TVCG.2012.250 |
We propose a method to highlight query hits in hierarchically clustered collections of interrelated items such as digital libraries or knowledge bases. The method is based on the idea that organizing search results similarly to their arrangement on a fixed reference map facilitates orientation and assessment by preserving a user's mental map. Here, the reference map is built from an MDS layout of the items in a Voronoi treemap representing their hierarchical clustering, and we use techniques from dynamic graph layout to align query results with the map. The approach is illustrated on an archive of newspaper articles. Forschungszusammenhang (Projekte) |
Name | Kennziffer | Beschreibung | Laufzeit |
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Deutsche Forschungsgemeinschaft | 501/09 | GRK 1042/2 | 01.01.2009 – 30.06.2013 |
Deutsche Forschungsgemeinschaft | 530/04 | GRK 1042/1 | 01.07.2004 – 31.12.2008 |
Laufzeit: | 01.07.2004 – 30.06.2013 |