SGER: Efficient Support for Mining Queries in Data Stream Management Systems

对海量数据流的连续查询的有效支持在许多应用领域至关重要，包括发布/订阅、流量监控、传感器网络和算法交易。因此，设计数据流管理系统（DSMS）来高效可靠地支持此类查询是当前研究的一个充满活力的领域。不幸的是，DSMS还不能有效地支持从数据流中提取新模式和知识所需的非常复杂的挖掘查询——尽管它们在重要的应用程序中是必需的，例如入侵检测和其他安全任务。设计用于支持挖掘任务的DSMS称为归纳DSMS（因为它们从数据中归纳出新的知识）。该项目的目标是通过(i)设计更快的数据流挖掘算法，（ii）扩展DSMS以支持用DSMS查询语言表达的有效挖掘任务，以及（iii）构建归纳DSMS原型并在数据挖掘测试平台上对其进行评估来开发归纳DSMS的启用技术。数据挖掘技术正在对各种应用领域产生重大影响，包括商业、安全和科学。然而，挖掘代表信息时代命脉的海量数据流已被证明是非常困难的：这是解决这一挑战的第一个项目。一旦电感式DSMS的愿景成为现实，广泛的科学、教育和经济活动将大大受益。项目资金将用于支持博士生进行DSMS和数据挖掘方面的研究。这项新技术将丰富几门研究生课程。通过出版物、报告和演示进行传播：http://wis.cs.ucla.edu/idsms。

Efficient support for continuous queries on massive data streams is critical in many application areas, including publish/subscribe, traffic monitoring, sensor networks, and algorithmic trading. Thus, designing data stream management systems (DSMS) to support such queries efficiently and reliably represents a vibrant area of current research. Unfortunately, DSMS cannot yet support efficiently the very complex mining queries required to extract new patterns and knowledge from data streams-- although they are needed in important applications, such as intrusion detection and other security tasks. DSMS designed to support mining tasks are called Inductive DSMS (since they induce new knowledge from data). This project's objective is to develop the enabling technology for Inductive DSMS by (i) designing faster data stream mining algorithms, and (ii) extending DSMS to support efficiently mining tasks expressed in the DSMS query language, and (iii) building an Inductive DSMS prototype and evaluating it on data mining testbeds. Data mining technology is having a major impact on diverse applications domains, including business, security, and science. However, mining the massive data streams that represent the lifeblood of the information age has proven very difficult: this is the first project addressing this challenge. A broad range of scientific, educational, and economic activities will benefit greatly once the vision of Inductive DSMS becomes reality. Project funds will support PhD students pursuing research on DSMS and data mining. The new technology will enrich several graduate courses. Dissemination is through publications, reports, and demos available from: http://wis.cs.ucla.edu/idsms.