Summary Extraction on Data Streams in Embedded Systems
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More and more data is created by humans and cyber-physicalsystems having sensing, acting and networking capabilities. Together, these systems form the Internet of Things (IoT). The realtime analysis of its data may provide us with valuable insights about the complex inner processes of the IoT. Moreover, these insights offer new opportunities ranging from sensor monitoring to actor control. The volume and velocity of the data at the distributed nodes challenge human as well as machine monitoring of the IoT. Broadcasting all measurements to a central node might exceed the network capacity as well as the resources at the central node or the human attention span. Hence, data should be reduced already at the local nodes such that the submitted information can be used for efficient monitoring. There are several methods that aim at data summarization ranging from clustering, aggregation to compression. Where most of the approaches transform the representation, we want to select unchanged data items from the data stream, already while they are generate by the cyber-physical system and atthe cyber-physical system. The observations are selected independent of their frequencies. They are meant to be efficiently transmitted. The ideal case is that no important measurement is missing in the selection and that no redundant items are transmitted. The data summary is easily interpreted and is available in realtime. We focus on submodular function maximization due to its strong theoretical background. We investigate its use for data summarization and enhance the Sieve-Streaming algorithm for data summarization on data streams such that it delivers smaller sets with high recall.
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