III: Small: Complex Event Analytics

III：小：复杂事件分析

• 批准号: 1018443
• 负责人: Elke Rundensteiner
• 金额: $ 49.99万
• 依托单位: Worcester Polytechnic Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018443&HistoricalAwards=false
• 关键词: III; Small; Complex; Event; Analytics

Recent advances in sensor technologies and expansion of wired andwireless communication protocols enable us to continuously collectinformation about the physical world, resulting in a rich set of novelservices. The ability to infer relevant patterns from these eventstreams in real-time and at various levels of abstractions to makenear instantaneous decisions is crucial for a wide range of missioncritical applications ranging from real-time crisis management tosecurity. This project designs, implements, and evaluates a novelcomplex event processing methodology, henceforth called Complex EventAnalytics (CEA). CEA integrates the capabilities of pattern matchingfrom complex event processing with the power of multi-level analysisfrom static OLAP engines to provide multi-dimensional sequentialpattern analysis over high-speed event streams. The CEA Modelcombines CEP and OLAP techniques for efficient multi-dimensional eventpattern analysis at different abstraction levels. Based oninterrelationships in both concept and pattern refinement amongqueries, sequence queries are composed into an integrated eventpattern hierarchy. OLAP like operations enable analysts to navigatefrom one E-cuboid to another in this event analytics space. CEAoptimization strategies, including rewriting rules, physicaloperators, and cost-based search algorithms, achieve scalable eventprocessing. CEA offers high-performance analytics by maximal sharedprocessing of event pattern queries. Experimental studies compare theCEA solution to the state-of-the-art, including traditional streamquery systems and customized event engines. Intellectual merit liesin the design, development and evaluation of a novel Complex EventAnalytics technology for real-time event stream analysis, -- a perfectmiddle ground offering both the sophisticated power of patternmatching found in modern event processing systems and the capabilityof online analytic techniques at multiple levels of abstraction ofOLAP engines. CEA impacts society by facilitating a broad range ofstream-centric applications ranging from monitoring of hygienecompliance to prevent the spread of infectuous diseases in medicalsettings to business intelligence processing, and by integratingproject activities with education.For further information see the project web site at the URL:http://davis.wpi.edu/dsrg/PROJECTS/CEA

传感器技术的最新进展以及有线和无线通信协议的扩展使我们能够不断收集有关物理世界的信息，从而产生了丰富的小说服务。 从实时和各个级别的抽象级别到Makenear瞬时决策中推断出相关模式的能力对于从实时危机管理局限性等广泛的任务批判性应用至关重要。 此项目设计，实施和评估了一种新颖的事件处理方法，因此称为复杂的EventAnalytics（CEA）。 CEA将模式匹配的复杂事件处理的功能与从静态OLAP发动机的多级分析的功能相结合，以在高速事件流中提供多维顺序分析。 CEA ModelCombines CEP和OLAP技术，用于在不同的抽象水平上有效的多维事件分析。基于Quermequies中概念和模式改进的分区关系，序列查询被组成为集成的事件模式层次结构。 OLAP类似操作使分析师能够在此事件分析空间中从一个电子生物导航到另一个E-Cuboid。 CeAptimization策略，包括重写规则，物理运输器和基于成本的搜索算法，可以实现可扩展的事件处理。 CEA通过对事件模式查询的最大共享处理提供高性能分析。 实验研究将thecea解决方案与最先进的解决方案进行了比较，包括传统的流Query系统和定制的事件引擎。 智力优点在于实时事件流分析的新型复杂的EventAnalytics技术的设计，开发和评估，这是一个完美的地面，可在现代事件处理系统中发现的图案匹配的复杂力量，以及在多个级别的抽象ofolap发动机的在线分析技术的能力。 CEA通过促进从监测Hygienecsilliance的监视到防止感染性疾病在医疗程序中的传播到商业智能处理，以及通过教育的更多信息，请参阅url：http：///davis.wpi.edu.edu.edu.edu.edu.edu. eedu u/dsrg/ppiu/dsrg/p，