Next-Generation Issues in Data Stream Management Systems

数据流管理系统的下一代问题

• 批准号: 0414762
• 负责人: Jennifer Widom
• 金额: $ 94.97万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-15 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414762&HistoricalAwards=false
• 关键词: Next; Generation; Issues; Data; Stream

ABSTRACTNSF 0414762Many modern applications must contend with continuous, unbounded, possibly rapid and time-varying streams of data, in addition to or instead of the more traditional bulk-loaded, periodically updated, data sets. In the presence of data streams, evaluating queries or other analyses over the data becomes a long-running, continuous process, rather than the typical one-time queries and analyses supported by traditional database systems. More generally, event-based, reactive processing as a computing paradigm is becoming more and more prevalent, spanning a wide variety of application areas. Continuous queries over data streams are an intuitive and effective approach to data management within this paradigm. Data-intensive applications of this type include network traffic monitoring and engineering, analysis of sensor network data, monitoring call-detail records or charge-card transactions for fraud detection, online analysis of financial data, manufacturing monitoring and control, and others. The original work focused on basic functionality of a streaming data prototype, but additional issues have become evident as applications have been deployed. These include query processing issues, such as approximate techniques to handle system overload, performance issues based on scalability problems and issues dealing with the distributed nature of the data processing. In addition, new work on algorithms for transparent querying of multiple web services and processing of streams of approximate or uncertain data will be explored. Because stream applications frequently maintain layers of derived data products, it is particularly important to capture the connections between derived and raw data to insure effective data maintenance and analysis.The history and scope of the STREAM project, coupled with the current popularity of the research area in general, positions the proposed work to have wide impact on both research and education. The project provides Ph.D.-level research and education for numerous graduate students, as well as research experience for a number of undergraduates. The graduate students give presentations and demonstrations of their work at large forums- up to hundreds of people. Research papers from the first phase of the project are being studied in graduate classes at several institutions, and the PIs have been organizing biannual meetings that bring together several data streams research groups. The STREAM project initiated and curates a repository of data stream application specifications, and will continue to expand this repository as an important part of the proposed work. Finally, the software developed is made available for download, and a convenient web-accessible server is provided so that anyone can try out the system without the inconvenience of downloading and installing the software.

AbstractNSF 0414762经纪现代应用程序必须与连续的，无限的，可能是快速且随时间变化的数据流，除了或更传统的批量加载，定期更新的数据集外，还必须抗衡数据集。在存在数据流的情况下，对数据进行评估或其他分析成为一个长期运行，连续的过程，而不是传统数据库系统支持的典型的一次性查询和分析。更一般而言，基于事件的反应处理作为计算范式越来越普遍，跨越了各种各样的应用领域。对数据流的连续查询是该范式中数据管理的直观有效的方法。这种类型的数据密集型应用程序包括网络流量监控和工程，传感器网络数据分析，监视呼叫记录或用于欺诈检测的费用卡交易，财务数据的在线分析，制造业监控和控制等。 原始工作着重于流数据原型的基本功能，但是随着应用程序的部署，其他问题变得显而易见。 其中包括查询处理问题，例如处理系统超负荷的近似技术，基于可伸缩性问题的性能问题以及与数据处理的分布式性质有关的问题。 此外，将探讨用于多个Web服务透明查询算法的新工作以及近似或不确定数据流的处理。 由于流应用程序经常维护派生数据产品的层，因此捕获派生数据和原始数据之间的连接以确保有效的数据维护和分析尤为重要。流项目的历史和范围，再加上研究领域的当前普及，将拟议的工作定位为对研究和教育产生广泛影响。该项目为众多研究生提供博士学位研究和教育，并为许多本科生提供研究经验。研究生在大型论坛上进行了演讲和演示 - 最多数百人。该项目第一阶段的研究论文正在几个机构的研究生班上研究，PI一直在组织双年会议，以汇集几个数据流研究小组。流项目启动并策划了数据流应用程序规范的存储库，并将继续扩展该存储库作为拟议工作的重要组成部分。最后，开发的软件可供下载，并提供了便捷的Web访问服务器，以便任何人都可以尝试系统，而无需下载和安装软件。