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On event detection from spatial time series for urban traffic applications

: Souto, G.; Liebig, T.


Michaelis, S.:
Solving large scale learning tasks : Challenges and algorithms; Essays dedicated to Katharina Morik on the occasion of her 60th birthday, Festschrift
Cham: Springer International Publishing, 2016 (Lecture Notes in Computer Science 9580)
ISBN: 978-3-319-41705-9 (Print)
ISBN: 978-3-319-41706-6 (Online)
Aufsatz in Buch
Fraunhofer ISST ()

Since the last decades the availability and granularity of location-based data has been rapidly growing. Besides the proliferation of smartphones and location-based social networks, also crowdsourcing and voluntary geographic data led to highly granular mobility data, maps and street networks. In result, location-aware, smart environments are created. The trend for personal self-optimization and monitoring named by the term âquantified selfâ will speed-up this ongoing process. The citizens in conjunction with their surrounding smart infrastructure turn into âliving sensorsâ that monitor all aspects of urban living (traffic load, noise, energy consumption, safety and many others). The "Big Data"- based intelligent environments and smart cities require algorithms that process these massive amounts of spatio-temporal data. This article provides a survey on event processing in spatio-temporal data streams with a special focus on urban traffic.