III: Small: QUEST: An Integrated Query and Event System on Noisy Streams and Tables

III：小：QUEST：一个关于嘈杂流和表的集成查询和事件系统

• 批准号: 1319600
• 负责人: Tingjian Ge
• 金额: $ 39.09万
• 依托单位: University of Massachusetts Lowell
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1319600&HistoricalAwards=false
• 关键词: III; Small; QUEST; Integrated; Query

Sequence and order are prevalent in data. In particular, time, as often perceived as the "fourth dimension", plays a pivotal role in data streams as well as in stored time series data. The ability to define events, detect and query complex events over noisy sequence data is a much needed addition to the standard data management systems and SQL. This is because complex event detection is about discovering the correlation and co-occurrence of many tuples in the same relation - different from the cursor model in SQL, and because it is sensitive to sequence errors. The goal of this research is to tightly integrate SQL queries and complex event detection over noisy streams and stored tables in a high-performing system. Specific techniques include: (1) Using a query suite to encapsulate the interactions between SQL queries and complex event detection, and proposing a comprehensive semantics for complex events; (2) Incorporating correlated error models into a novel complex event matching algorithm; (3) Designing more efficient algorithms that only get the top-k highest probability matching paths, as well as algorithms that handle other variants of semantics; and (4) Devising several indexing mechanisms for stored sequence tables, and performing overall system optimization for both streams and tables.The QUEST project has applications in dietary and exercise monitoring/analysis, ECG monitoring, shopping behavior study, security monitoring, and travel pattern discovery, among many others. These applications play a key role in the nation's economy (business intelligence), health, and security. The project greatly enriches the course contents of the undergraduate directed research course in addition to a new graduate course. Further information on the project can be found on the project web page: http://www.cs.uml.edu/~ge/projects/quest/ .

序列和顺序在数据中很普遍。特别是，时间通常被视为“第四维度”，在数据流以及存储的时间序列数据中发挥着关键作用。在嘈杂的序列数据上定义事件、检测和查询复杂事件的能力是标准数据管理系统和 SQL 急需的补充。这是因为复杂事件检测是关于发现同一关系中许多元组的相关性和共现性——与 SQL 中的游标模型不同，并且因为它对顺序错误很敏感。这项研究的目标是在高性能系统中紧密集成 SQL 查询和对噪声流和存储表的复杂事件检测。具体技术包括：（1）使用查询套件封装SQL查询和复杂事件检测之间的交互，并提出复杂事件的综合语义； (2) 将相关误差模型纳入新颖的复杂事件匹配算法中； （3）设计更高效的算法，仅获得top-k最高概率的匹配路径，以及处理其他语义变体的算法； (4)为存储的序列表设计多种索引机制，并对流和表进行整体系统优化。QUEST项目在饮食和运动监测/分析、心电图监测、购物行为研究、安全监测和出行模式方面有应用发现等等。这些应用程序在国家经济（商业智能）、健康和安全中发挥着关键作用。该项目除了开设新的研究生课程外，还极大地丰富了本科生定向研究课程的课程内容。有关该项目的更多信息可以在项目网页上找到：http://www.cs.uml.edu/~ge/projects/quest/。