A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications.
Future Generation Computer Systems(2015)
摘要
Healthcare scientific applications, such as body area network, require of deploying hundreds of interconnected sensors to monitor the health status of a host. One of the biggest challenges is the streaming data collected by all those sensors, which needs to be processed in real time. Follow-up data analysis would normally involve moving the collected big data to a cloud data center for status reporting and record tracking purpose. Therefore, an efficient cloud platform with very elastic scaling capacity is needed to support such kind of real time streaming data applications. The current cloud platform either lacks of such a module to process streaming data, or scales in regard to coarse-grained compute nodes.
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关键词
Adaptive MapReduce,Big data,Healthcare scientific applications,Kalman filter,Parallel processing
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