CAREER: Robust Processing of Data Streams in Real Time

职业：实时数据流的鲁棒处理

• 批准号: 1253908
• 负责人: Shrideep Pallickara
• 金额: $ 32.12万
• 依托单位: Colorado State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-01 至 2019-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1253908&HistoricalAwards=false
• 关键词: CAREER; Robust; Processing; Data; Streams

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.

该项目研究了对医学传感器数据流集合的处理进行调度的问题，目的是提供高置信度的每分组服务保证，该服务保证对流生成中的可变性以及在处理流的分布式资源集处的负载的伴随变化是稳健的。考虑到最优流调度的NP-Hard复杂性以及需要处理本质上是随机的流，服务保证是概率的。该方法利用统计和机器学习技术，利用与应用程序无关和与应用程序相关的混合功能，在具有动态利用率配置文件的资源集合上自适应地编排流处理，同时保留在重负载下为多个并发应用程序确定处理优先级的能力。来自临床和辅助生活环境的数据被用来评估解决方案的有效性。医疗保健和国土安全可以从这项研究以及实验科学中受益。医疗成本飙升的同时，医疗和辅助生活环境中的电子监控设备也在激增，这些设备可以生成患者数据流。及时监测和分析这些数据流可以及早发现紧急情况并改善患者预后，但失败可能是致命的。自动化医疗数据流处理的效率和稳健性的提高转化为更低的成本和更好的结果。国土安全领域也有类似的机会，必须实时处理化学和生物传感器数据，以进行威胁评估。作为这项研究的一部分而生产的开源软件，可以在任意数量的机器上配置，以处理各种环境中的大量数据流，降低了需要在其应用程序中处理观测数据的科学家的准入门槛。该项目还将为学生提供教育机会，以及旨在改善美洲原住民学生对数学概念的同化的中学推广活动。