III: Small: Complex Event Analytics

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，从而产生丰富的新颖服务。 从这些事件流中实时推断出相关模式的能力，以及在各种抽象级别上做出即时决策的能力，对于从实时危机管理到安全性的各种关键任务应用至关重要。 该项目设计、实现和评估了一种新颖的复杂事件处理方法，此后称为复杂事件分析（CEA）。 CEA将复杂事件处理中的模式匹配功能与静态OLAP引擎的多层次分析功能相结合，在高速事件流上提供多维序列模式分析。 CEA模型结合了CEP和OLAP技术，在不同的抽象层次上进行有效的多维事件模式分析。基于概念和模式细化查询的相互关系，序列查询被组合成一个集成的事件模式层次结构。 类似OLAP的操作使分析师能够在事件分析空间中从一个E-cuboid导航到另一个。 CEA优化策略，包括重写规则，物理运算符和基于成本的搜索算法，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来提供高性能分析。 实验研究比较了CEA解决方案的国家的最先进的，包括传统的流查询系统和定制的事件引擎。 智力价值在于设计，开发和评估一种新的复杂事件分析技术，用于实时事件流分析，-一个完美的中间地带，提供了现代事件处理系统中发现的模式匹配的复杂功能和在线分析技术在OLAP引擎的多个抽象层次上的能力。 CEA通过促进广泛的以流为中心的应用程序来影响社会，这些应用程序包括从监测商业环境以防止医疗环境中传染病的传播到商业智能处理，以及将项目活动与教育相结合。欲了解更多信息，请访问项目网站：http://davis.wpi.edu/dsrg/PROJECTS/CEA

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