III: Small: High-Performance Complex Processing of Continuous Uncertain Data

三：小：连续不确定数据的高性能复杂处理

• 批准号: 1218524
• 负责人: Yanlei Diao
• 金额: $ 49.6万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218524&HistoricalAwards=false
• 关键词: III; Small; Performance; Complex; Processing

The objective of this project is to design and develop a data management system that supports query processing on continuous uncertain data by returning a full probability distribution of query output and optimizes such processing for performance. This project includes four thrusts: (1) supporting continuous uncertain data processing using both the traditional relational model and the array model; (2) addressing complex correlation that arises in continuous uncertain data processing using new statistical graphical models; (3) supporting arbitrary user-defined functions, besides standard query operations, by exploring advanced techniques such as Gaussian processes and functional interpolation; and (4) developing a prototype system and evaluating it using real-world applications. Expected results include statistical models and techniques, data storage schemes, query processing and optimization techniques, and a publicly available prototype to fully support query processing on continuous uncertain data. The results of the project can benefit applications such as severe weather monitoring and computational astrophysics, as well as the broader scientific community. Since applications such as tornado detection may trigger actions based on derived information, the ability to characterize uncertainty of output may result in significant social impacts. This project also integrates research and education with curriculum development and engaging women in research through college outreach and CRA's distributed mentor program. The results of the project are disseminated at the project web site: http://claro.cs.umass.edu.

该项目的目标是设计和开发一个数据管理系统，通过返回查询输出的完整概率分布来支持对连续不确定数据的查询处理，并优化此类处理的性能。该项目包括四个主旨：（1）使用传统关系模型和数组模型支持连续不确定数据处理； （2）使用新的统计图形模型解决连续不确定数据处理中出现的复杂相关性； （3）通过探索高斯过程和函数插值等先进技术，除了标准查询操作之外，还支持任意用户定义的函数； (4) 开发原型系统并使用实际应用对其进行评估。预期结果包括统计模型和技术、数据存储方案、查询处理和优化技术以及完全支持连续不确定数据查询处理的公开原型。该项目的结果可以使恶劣天气监测和计算天体物理学等应用以及更广泛的科学界受益。由于龙卷风检测等应用可能会根据派生信息触发行动，因此表征输出不确定性的能力可能会产生重大的社会影响。该项目还将研究和教育与课程开发相结合，并通过大学外展和 CRA 的分布式导师计划让女性参与研究。该项目的结果在项目网站上发布：http://claro.cs.umass.edu。