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Stream computing, also known as data stream processing, has emerged as a new processing paradigm that processes incoming data streams from tremendous numbers of sensors in a real-time fashion. Data stream applications must have low latency even when the incoming data rate fluctuates wildly. This is almost impossible with a local stream computing environment because its computational resources are finite. To address this kind of problem, the authors have devised a method and an architecture that transfers data stream processing to a Cloud environment as required in response to the changes of the data rate in the input data stream.
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