Willkommen auf den Seiten des Lehrstuhls "Internet-Technologien und -Systeme" von Prof. Dr. Christoph Meinel und seinem Team. Sie finden hier Informationen über unser Lehrangebot und unsere Forschungsaktivitäten in den Bereichen Security und Knowledge Engineering sowie Innovationsforschung.

Der Lehrstuhl von Prof. Dr. Christoph Meinel bietet Lehrveranstaltungen in folgenden Bereichen an: Internet und Web-Technologien, (Diskrete) Mathematik und Logik, IT-Security und Internet Security, Komplexitätstheorie und Informationssicherheit sowie Design Thinking.

Im Fokus der Forschungen am Lehrstuhl von Prof. Dr. Christoph Meinel zum Thema Knowledge Management und Engineering steht die Frage, wie aus den im Internet und anderen Quellen massenhaft zur Verfügung stehenden digitalen Daten, "Big Data", neues Wissen generiert werden kann.

In Security and Trust Engineering our research and development work is mainly focused on: Network & Internet Security, Cloud and SOA-Security (SOA - Service Oriented Architectures) and Security Awareness.

Aus den IT-technologischen Forschungen des Teams um Prof. Dr. Christoph Meinel im Bereich der Internet-Technologien und -System und der Innovationsforschung werden auch neue Ideen geboren für innovative Anwendungen und Systeme für das Internet der Zukunft.

Hier finden Sie Links zu allen unseren Textbüchern und den mehr als 400 (begutachteten) Konferenz- und Journal-Beiträgen.

Complete List of Conference Papers of the chair of Prof. Dr. Christoph Meinel

Here you can find all our peer-reviewed conference papers.

You can also find sub-lists of our peer-reviewed conference papers focusing on

Security and Trust Engineering
Web 3.0: semantic web, social web, service web
Next-generation Internet applications in
Innovation and Design Thinking research
Theoretical Computer Science: Computational Complexity and BDDs

Recognizing Compound Events in Spatio-Temporal Football Data

Richly, Keven; Bothe, Max; Rohloff, Tobias; Schwarz, Christian in International Conference on Internet of Things and Big Data (IoTBD) Seite 27-35 . 2016 .

In the world of football, performance analytics about a player’s skill level and the overall tactics of a match are supportive for the success of a team. These analytics are based on positional data on the one hand and events about the game on the other hand. The positional data of the ball and players is tracked automatically by cameras or via sensors. However, the events are still captured manually by human, which is time-consuming and error-prone. Therefore, this paper introduces an approach to detect events based on the positional data of football matches. We trained and aggregated the machine learning algorithms Support Vector Machine, K-Nearest Neighbours and Random Forest, based on features, which were calculated on base of the positional data. We evaluated the quality of our approach by comparing the recall and precision of the results. This allows an assessment of how event detection in football matches can be improved by automating this process based on spatio-temporal data. We discovered, that it is possible to detect football events from positional data. Nevertheless, the choice of a specific algorithm has a strong influence on the quality of the predicted results.

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