Web服务环境中的复杂事件处理*

事件驱动的SOA结合使用了SOA的请求—响应模式和事件驱动架构的事件发布—提交模式。服务和事件处理的结合产生了更好的敏捷性,对于现代业务流程中产生的海量事件流,通过复杂事件处理提供即时过滤、聚集和关联事件的功能,使解决方案能够以极快的速度迅速标志出有价值的事件,使企业具备对此作出即时反应的能力。提出Web服务环境中的复杂事件处理架构,在基于WSRF(Web service resource properties framework)的环境下实现Web服务之间事件通知机制的规范。应用通知生产者和通知消费者来     

基于复杂事件处理的算法交易研究

在目前的资本市场,算法交易得到了广泛的应用.通过实时分析大量的金融市场数据,算法交易能够合理地决定交易的时机、价格、数量等交易的各个方面.现有的一般架构通常采用存储-索引-查询的数据库建模方法,但是这不能满足实时处理要求.复杂事件处理是构建和管理由事件驱动的信息系统的一种新兴技术.它用来处理大量的事件流,实时分析这些事件并且响应感兴趣的事件.针对于此,提出了基于复杂事件处理的算法交易架构模型,并在此基础上,提出了对应的事件模型以及设计了一个有效的检测复杂事件的算法.     

复杂事件处理技术及其应用综述

近年来,处理更高级别的事件(通常被称为业务事件)的需求迅速扩大,复杂事件处理(Complex Event Processing,CEP)的发展满足了这些新的市场需求。CEP能结合来自多个数据源的数据来推断更为复杂的事件或模式。CEP的目标是从事件流中找出有意义的事件(如机会或威胁),并尽快做出反应。文本介绍了CEP的研究现状,包括通用的参考架构,代表性的CEP系统及其事件处理语言,以及最常见的应用场景。     

基于CEP的外汇自动成交系统的设计与实现

针对外汇自动交易行业对数据快速变动的敏感性要求,建立了一个基于复杂事件处理(CEP)和云计算技术的托管平台.在此平台上,托管用户可以通过云计算提供的API,设定自己的交易方式和处理方法,实现了用户的托管管理和动态事件绑定,降低了用户对本地硬件资源和网络带宽的要求.实验结果表明,通过CEP和托管特定事件触发方式,对于相同的事件分析和逻辑分析,平台响应速度提高5～10倍.     

复杂事件处理在工作流管理监控模块中的应用

工作流技术已经成为计算机应用领域的一个研究热点。国际工作流管理联盟(WFMC)提出了工作流参考模型,包括:流程定义工具、工作流执行服务、工作流客户端应用、直接调用应用和管理监控工具。复杂事件处理是一种从基于消息的分布式系统中提取信息的新技术。在此文中详细介绍怎样将复杂事件处理应用到工作流管理监控模块中,实现了更高效的管理和监控工作流事件。     

复杂事件处理技术及其在物流物联网中的应用

复杂事件处理(CEP)技术是目前对高速事件流中蕴含信息进行实时分析的最佳技术,重点应用于事件驱动架构系统中,能够帮助业务应用实现智能化处理。为报告其研究发展现状,在给出CEP基本含义及显著特征的基础上,首先提出了由9个模块组成的CEP系统通用结构模型;然后综述了模型中重要部分相关关键技术的分类与形式化基础;同时设计了CEP在物流物联网中综合应用的框架模型,由此得出了CEP技术的优点及作用;最后指出了目前存在的不足及未来的研究方向。从理论与实践两方面对CEP技术进行了系统、详细、深入的剖析,以期推动其继续发展。     

基于CEP的可持久化事件处理方案

针对现行复杂事件处理CEP(Complex Event Processing)平台没有提供完善的异常恢复、消息事件可持久化机制的缺点,以系统输出是否实时为标准,分两类分析并抽象出系统处理流程,进而提出了两种基于现行复杂事件处理平台的较通用的增强方案,并给出可广泛适用的处理过程描述,同时也对两类方案的优缺点进行了比较.     

Ceper:一个高性能复杂事件处理引擎

复杂事件处理技术从连续的输入事件流中分析并提取出满足特定模式的事件序列。它将传统先存储静态数据再进行处理和挖掘的数据处理方法,转变为实时处理动态事件流,更加符合真实世界的数据处理模式,能够高效及时地从数据流中过滤出相对少量的具有应用价值的数据。文章设计并实现了一个高性能复杂事件处理引擎——Ceper,提出了Ceper的事件处理模型,展示了它的系统结构和模式匹配树的方法 PMT,介绍了Ceper的实现的关键技术,并进行了一定的测试。     

RFID复杂事件处理技术

随着RFID技术的发展,RFID应用正无所不在。通过对RFID数据的深入处理和分析,可以发现更复杂的复合事件和隐含知识,从而有效地支持事件监控、事件预警等先进应用。由于RFID的特殊性,依靠现有的主动数据库技术和数据流管理技术难以实现高效的RFID事件检测和处理。分析了RFID数据的特点,归纳和总结了RFID复杂事件处理的最新技术,讨论了一些亟待解决的新问题,主要有RFID数据清洗方法、以数据为中心的检测技术、以事件为中心的检测技术,以及复杂事件处理系统等,并对今后的研究重点进行了展望。     

一种复杂事件处理语言的自定义函数功能扩展

为使复杂事件处理语言满足股票分析、故障监控等行业的细粒度查询需求,本文为一种复杂事件处理语言CEStream扩展支持细粒度数据处理的用户自定义函数功能。函数通过对数据逐个处理和函数重载实现细粒度处理功能。同时函数内可调用原有语句,给用户提供基础的模块化功能,提高语言的复用性。实验结果表明扩展的函数功能使语言能够支持更细粒度的查询需求,同时在完成相同查询需求时使用函数相对于使用原有语句完成系统吞吐量基本相同。     

Architecture, implementation and application of complex event processing in enterprise information systems based on RFID

Enterprises have to be increasingly agile and responsive to address the challenges posed by the fast moving market. With the software architecture evolving into service-oriented architecture, and the adoption of Radio Frequency Identification (RFID), event processing can fit well in enterprise information systems in terms of facilitation of event aggregation into high level actionable information, and event response to improve the responsiveness. To make it more applicable, the architecture of event processing in enterprise information systems is proposed; event meta model is put forth, and the rules are defined. To improve the detect efficiency, classification and partition of event instance is utilized. We have implemented the event processing mechanism in enterprise information systems based on RFID, including the data structures, optimization strategies and algorithm that is considered as one of the contributions. The performance evaluations show that the method is effective in terms of scalability and the capability of event processing. Complex event processing can improve operation efficiency and discover more actionable information, which is justified by the application.

基于SOA的事件协同感知模型

在现有面向服务架构（SOA）实现中,Web服务之间无法直接协同。为了完成协同工作,SOA的各个构件必须具有对系统全局的感知能力。该文采用一个事件信息组件,在SOA中引入事件驱动架构,搜集各个Web服务的调用信息,利用复杂事件处理技术对其进行整理后,发送给SOA构件订阅者,达到协同感知的目的。     

基于复杂事件处理技术的RFID系统数据分析

数据处理技术是射频识别技术(RadioFrequencyIdentification,RFID)中的关键技术之一,也是近年来RFID领域研究的热点。该文采用复杂事件处理技术对RFID系统中的大量分散数据进行分析处理,用多层视图、事件过虑器、事件总结器、事件之间的关系等技术对大量数据在不同层次进行分析,得到优化的分析结果。     

CEStream:一种复杂事件流处理语言

复杂事件处理是支持大数据处理的流式计算平台的核心技术之一。CEStream语言作为一种新型的事件流处理语言,支持分布式环境下的复杂事件处理。该语言以XML等层次化数据为数据模型,为复杂事件检测提供了一种正规树模式匹配功能,并且支持结构连接和正规式匹配。同时,针对分布式的多个事件流,其能够将各个事件源模式匹配的结果按照时间顺序再一次进行正规式模式匹配,满足多源组合型复杂事件的检测需求,具有较强的事件处理能力。为了实现CEStream语言,研制了一个基于流数据处理集群和远端查询代理的执行引擎系统。该系统通过远程查询代理实现基于正规树模式的事件检测,通过流处理集群完成多源组合型复杂事件处理。实验表明,该系统实现了CEStream语言,有效地限制了各个节点之间的通信量,充分利用了集群的计算能力,整体性能能够满足应用需求。     

ARCHITECT: A layered framework for classifying technologies of event-based systems

In the past decade, event-based systems (EBS) have received increasing attention from various communities. Central to these systems is the notion of event, which is often generally considered as “a happening of interest”. An EBS encompasses a large range of functionalities on various technological levels (e.g., language, execution, or communication). Existing approaches vary in the scope of implemented functionality and underlying technical concepts. For this reason, comparing EBS solutions is a challenging task. Furthermore, the lack of a unified view on EBS poses challenges to system architects in choosing and combining technologies for building event-based systems.     

A model-driven approach for facilitating user-friendly design of complex event patterns

Complex Event Processing (CEP) is an emerging technology which allows us to efficiently process and correlate huge amounts of data in order to discover relevant or critical situations of interest (complex events) for a specific domain. This technology requires domain experts to define complex event patterns, where the conditions to be detected are specified by means of event processing languages. However, these experts face the handicap of defining such patterns with editors which are not user-friendly enough. To solve this problem, a model-driven approach for facilitating user-friendly design of complex event patterns is proposed and developed in this paper. Besides, the proposal has been applied to different domains and several event processing languages have been compared. As a result, we can affirm that the presented approach is independent both of the domain where CEP technology has to be applied to and of the concrete event processing language required for defining event patterns.     

面向模具企业数据集成的事件驱动ESB框架研究*

分析了模具企业信息共享与数据集成的需求与特点,结合面向服务思想和事件驱动架构,提出了一种事件驱动型企业服务总线（enterprise service bus,ESB）框架;阐述了该框架的运行原理及实现技术,并在此框架基础上实现了基于SOA的模具企业设计制造集成平台数据集成服务。该框架满足模具企业事件驱动的业务特征,并采用总线式的集成模式和异步消息的方式,松散耦合地集成各种应用系统及数据,降低了应用集成的难度和成本,为中小型制造企业解决数据集成问题提供了新的方案。     

CEPSim: Modelling and simulation of Complex Event Processing systems in cloud environments

The emergence of Big Data has had profound impacts on how data are stored and processed. As technologies created to process continuous streams of data with low latency, Complex Event Processing (CEP) and Stream Processing (SP) have often been related to the Big Data velocity dimension and used in this context. Many modern CEP and SP systems leverage cloud environments to provide the low latency and scalability required by Big Data applications, yet validating these systems at the required scale is a research problem per se. Cloud computing simulators have been used as a tool to facilitate reproducible and repeatable experiments in clouds. Nevertheless, existing simulators are mostly based on simple application and simulation models that are not appropriate for CEP or for SP. This article presents CEPSim, a simulator for CEP and SP systems in cloud environments. CEPSim proposes a query model based on Directed Acyclic Graphs (DAGs) and introduces a simulation algorithm based on a novel abstraction called event sets. CEPSim is highly customizable and can be used to analyse the performance and scalability of user-defined queries and to evaluate the effects of various query processing strategies. Experimental results show that CEPSim can simulate existing systems in large Big Data scenarios with accuracy and precision.     

Mobiiscape: Middleware support for scalable mobility pattern monitoring of moving objects in a large-scale city

With the explosive proliferation of mobile devices such as smartphones, tablets, and sensor nodes, location-based services are getting even more attention than before, considered as one of the killer applications in the upcoming mobile computing era. Developing location-based services necessarily requires an effective and scalable location data processing technology. In this paper, we present Mobiiscape, a novel location monitoring system that collectively monitors mobility patterns of a large number of moving objects in a large-scale city to support city-wide mobility-aware applications. Mobiiscape provides an SQL-like query language named Moving Object Monitoring Query Language (MQL) that allows applications to intuitively specify Mobility Pattern Monitoring Queries (MPQs). Further, Mobiiscape provides a set of scalable location monitoring techniques to efficiently process a large number of MPQs over a large number of location streams. The scalable processing techniques include a (1) Place Border Index, a spatial index for quickly searching for relevant queries upon receiving location streams, (2) Place-Based Window, a spatial-purpose window for efficiently detecting primitive mobility patterns, (3) Shared NFA, a shared query processing technique for efficiently matching complex mobility patterns, and (4) Attribute Pre-matching Bitmap, an in-memory data structure for efficiently filtering out moving objects based on their attributes. We have implemented a Mobiiscape prototype system. Then, we show the usefulness of the system by implementing promising location-based applications based on it such as a ubiquitous taxicab service and a location-based advertising. Also, we demonstrate the performance benefit of the system through extensive evaluation and comparison.