This project investigates the problem of scheduling the processing of collections of streams of medical sensor data, with a goal of providing high-confidence per-packet service guarantees that are robust to variability in the stream generation and concomitant changes in the loads at the distributed set of resources where streams are processed. Given the NP-Hard complexity of optimal stream scheduling and the need to handle streams that are stochastic in nature, the service guarantees are probabilistic. The approach makes use of statistical and machine learning techniques, harnessing a mix of application-independent and application-dependent features to adaptively orchestrate stream processing over a collection of resources with dynamic utilization profiles while retaining the ability to prioritize processing under heavy load, for multiple concurrent applications. Data from clinical and assisted living settings are used to evaluate the efficacy of solutions.
Health care and homeland security can benefit from this research, as well as experimental science. Skyrocketing healthcare costs have coincided with the proliferation of electronic monitoring devices in medical and assisted living environments, which generate data streams of patient data. Timely monitoring and analysis of these streams can detect emergencies early and improve patient outcomes, but failure can be fatal. Improvements in the efficiency and robustness of automated medical data stream processing translate to lower costs and improved outcomes. There are analogous opportunities in homeland security, where chemical and biological sensor data must be processed in real-time for threat evaluation. Open-source software produced as part of this research, which can be configured over an arbitrary number of machines to process a large number of streams in a variety of settings, lowers entry barriers for scientists who need to process observational data in their applications. The project will also provide educational opportunities for students, and middle school outreach activities targeted at improving assimilation of mathematical concepts among Native American students.
