Distributed event processing for activity recognition
Stream-processing systems inevitably face unpredictable variations in incoming event loads. One way of handling this without affecting end-to-end performance metrics, will be to dynamically distribute event-processing on multiple computers and thus avail compute power for optimal performance. More precisely, data streams are processed in part or in parallel on multiple computers connected by a high bandwidth network. The number of computers being used is to be varied dynamically to cope with input load fluctuations. This paper uses data from ambient kitchen to make a preliminary assessment of performance advantages by distribution of real-time data stream processing. The motivation is to leverage cloud computing for optimal realtime event processing.