Reference : Graph-based Software Knowledge: Storage and Semantic Querying of Domain Models for Ru...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/32855
Graph-based Software Knowledge: Storage and Semantic Querying of Domain Models for Run-Time Adaptation
English
Hochgeschwender, Nico [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Schneider, Sven []
Voos, Holger mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Engineering Research Unit > ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)]
Bruyninckx, Herman []
Kraetzschmar, Gerhard []
Dec-2016
IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots SIMPAR, San Francisco, Dec 2016
83-90
Yes
International
IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)
Dec 13-16, 2016
IEEE
San Francisco
USA
[en] Robotics ; Software Engineering
[en] Software development for robots is a knowledge intensive exercise. To capture this knowledge explicitly and formally in the form of various domain models, roboticists have recently employed model-driven engineering (MDE) approaches. However, these models are merely seen as a way to support humans during the robot's software design process. We argue that the robots themselves should be first-class consumers of this knowledge to autonomously adapt their software to the various and changing run-time requirements induced, for instance, by the robot's tasks or environment. Motivated by knowledge-enabled approaches, we address this problem by employing a graph-based knowledge representation that allows us not only to persistently store domain models, but also to formulate powerful queries for the sake of run time adaptation. We have evaluated our approach in an integrated, real-world system using the neo4j graph database and we report some lessons learned. Further, we show that the graph database imposes only little overhead on the system's overall performance.
Researchers
http://hdl.handle.net/10993/32855
10.1109/SIMPAR.2016.7862379

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