CAREER: Novel Query Processing Techniques for Distributed Probabilistic Data

职业：分布式概率数据的新颖查询处理技术

• 批准号: 1200792
• 负责人: Feifei Li
• 金额: $ 49.74万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-29 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1200792&HistoricalAwards=false
• 关键词: CAREER; Novel; Query; Processing; Techniques

Data are increasingly generated, stored, and processed distributively. Meanwhile, when large amounts of data are generated, ambiguity, uncertainty, and errors are inherently introduced, especially in a distributed setup. It is best to represent such data in a distributed probabilistic database. In distributed data management, summary queries are useful tools for obtaining the most important answers from massive quantities of data effectively and efficiently, e.g., top-k queries, heavy hitters (aka frequent items), histograms and wavelets, threshold monitoring queries, etc. This project investigates novel query processing techniques for various, important summary queries in distributed probabilistic data.Broadly classified, this project examines both snapshot summary queries in static (i.e., no updates) distributed probabilistic databases, and continuous summary queries in dynamic (i.e., with updates) distributed probabilistic databases. A number of techniques are explored to design novel, communication and computation efficient algorithms for processing these queries. A distributed probabilistic data management system (DPDMS) prototype is implemented based on the query processing techniques developed in this project. This DPDMS is released to and used in practice by scientists and engineers from other science disciplines as well as industry.Graduate and undergraduate students, including those from minority groups, are actively involved in this project. Findings from the project have been integrated into different courses, demos, and educational projects. For further information, such as publications, data sets, source code, and education initiatives, please visit the project website at http://www.cs.fsu.edu/~lifeifei/dpdm.

数据越来越生成，存储和处理。同时，当生成大量数据时，固有地引入了歧义，不确定性和错误，尤其是在分布式设置中。最好在分布式概率数据库中表示此类数据。在分布式数据管理中，摘要查询是有用的工具，可有效，有效地从大量数据中获取最重要的答案，例如Top-K查询，重型击球手，重型击球手，频繁的项目（又称频繁项目），直方图和小波，阈值监视查询等。该项目对各种重要的数据进行了验证，这些项目概括了这些项目的分配数据，分配了分配的数据，这些数据是分配的，这些数据是分配的，这些数据是分配的，这些数据是分配的，这些数据库中的分配数据综合了群体，综合了群体，综合了群体，综合范围综述了群体，综合了数据，并综合了数据。快照摘要查询（即无更新）分布式概率数据库，以及动态（即带有更新）分布式概率数据库中的连续摘要查询。探索了许多用于处理这些查询的小说，通信和计算有效算法的技术。根据该项目中开发的查询处理技术，实现了分布式概率数据管理系统（DPDMS）原型。该DPDMS由其他科学学科以及行业的科学家和工程师发布并用于实践。研究生和本科生，包括来自少数群体的学生，都积极参与该项目。该项目的发现已纳入不同的课程，演示和教育项目。有关出版物，数据集，源代码和教育计划之类的更多信息，请访问项目网站http://www.cs.fsu.edu/~lifeififei/dpdm。