Uwe-Philipp Käppeler

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In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify(More)
Meta data - data about data - improves the value of the operational data by giving applications and users additional information on the data's origin, its precision or staleness. We outline the benefits of modeling meta data in context models: it can be used for resource finding, enhanced data selection, trust and data quality issues and sensor fusion. We(More)
The integration and usage of uncertain sensor data in workflows is a difficult problem. In this paper we describe these difficulties which result from the combination of very distinct areas. On the one hand, applications from area of measurement engineering manage sensors that capture data and annotate the data with technical meta data. On the other hand,(More)
An important aim of the current research effort in artificial intelligence and robotics is to achieve cooperative agent behavior for teams of robots in real-world scenarios. Especially in the RoboCup scenario , but also in other projects like Nexus, agents have to cooperate efficiently to reach certain goals. In the RoboCup project, cooperative team-play(More)
In this paper a learning algorithm for the automatic adaption of a situation template is presented. The approach strongly relies on human-machine interaction as user feedback is a substantial part to automatically adapt a global knowledgebase in this case. The work bases on the assumption of uncertain data and includes elements from the domain of Bayesian(More)
— This paper describes our stereo vision method which is combining an omnidirectional and a perspective camera. It was developed for our robot soccer team 1. RFC Stuttgart, which attends RoboCup competitions every year. The common approach to stereovision leads to high deviations from the real positions when it is not possible to synchronize the cameras for(More)