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- W2016799125 abstract "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." @default.
- W2016799125 created "2016-06-24" @default.
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- W2016799125 date "2011-07-11" @default.
- W2016799125 modified "2023-09-25" @default.
- W2016799125 title "Distributed event processing for activity recognition" @default.
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- W2016799125 doi "https://doi.org/10.1145/2002259.2002315" @default.
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