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ENABLING CAPTURE, TRANSMISSION AND RECONSTRUCTION OF RELATIVE CAUSITIVE CONTEXTURAL HISTORY FOR RESOURCE-CONSTRAINED STREAM COMPUTING APPLICATIONS
ENABLING CAPTURE, TRANSMISSION AND RECONSTRUCTION OF RELATIVE CAUSITIVE CONTEXTURAL HISTORY FOR RESOURCE-CONSTRAINED STREAM COMPUTING APPLICATIONS
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机译:在资源受限的流计算应用程序中捕获,传输和重建相对的因果关系上下文历史
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摘要
A scalable middleware for supporting energy-efficient, long-term remote health monitoring and the capture and transmission of relative causative contextual history where data is collected using physiological sensors and transported back to the middleware through a mobile device serving as a gateway. The key to energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring paradigm, where data collection episodes are triggered only when the system is determined to possess a specified set of causative contexts. The system supports on-demand collection of causative contextual history using a low-overhead provenance collection sub-system. In a preferred embodiment the behavior of this sub-system is configured using an application-defined context composition graph. The resulting causative context history stream provides valuable insight into the states and conditions surround sensor readings and allows improved human interpretation of the ‘episodic’ sensor data streams.
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