Management and Processing of Data Streams

数据流的管理和处理

• 批准号: 0118173
• 负责人: Jennifer Widom
• 金额: $ 44.5万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2005-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0118173&HistoricalAwards=false
• 关键词: Management; Processing; Data; Streams

For many recent applications, the concept of a data stream, possibly infinite, is more appropriate than a data set. By nature, a stored data set is appropriate when significant portions of the data are queried again and again, and updates are small and/or relatively infrequent. In contrast, a data stream is appropriate when the data is changing constantly (often exclusively through insertions of new elements), and it is either unnecessary or impractical to operate on large portions of the data multiple times. The goal of this research project is to develop models and techniques for the management and processing of data streams. Sampling, summarization, and online approximation algorithms will be employed to facilitate query processing and data mining over streams. The results of this project will provide efficient data stream techniques for data management, memory management, query processing, data mining, and data analysis. In addition, a software prototype will be developed for experimentation with algorithms and query processing, and as a testbed for some sample applications of significant scope, such as networking monitoring and traffic engineering, and medical monitoring data.

对于许多最近的应用，数据流的概念（可能是无限）比数据集更合适。从本质上讲，当大部分数据被一次又一次查询时，存储的数据集是合适的，并且更新很小和/或相对较少。相反，当数据不断变化时（通常是通过插入新元素的插入），数据流是合适的，并且多次在大部分数据上操作是不必要的或不切实际的。该研究项目的目的是开发用于数据流的管理和处理的模型和技术。采样，汇总和在线近似算法将采用用于促进流的查询处理和数据挖掘。该项目的结果将为数据管理，内存管理，查询处理，数据挖掘和数据分析提供有效的数据流技术。此外，将开发一个软件原型，用于使用算法和查询处理的实验，并作为某些显着范围的示例应用程序的测试台，例如网络监控和交通工程以及医疗监控数据。