A decentralized approach for mining event correlations in distributed system monitoring

Nowadays, there is an increasing demand to monitor, analyze, and control large scale distributed systems. Events detected during monitoring are temporally correlated, which is helpful to resource allocation, job scheduling, and failure prediction. To discover the correlations among detected events, many existing approaches concentrate detected events into an event database and perform data mining on it. We argue that these approaches are not scalable to large scale distributed systems as monitored events grow so fast that event correlation discovering can hardly be done with the power of a single computer. In this paper, we present a decentralized approach to efficiently detect events, filter irrelative events, and discover their temporal correlations. We propose a MapReduce-based algorithm, MapReduce-Apriori, to data mining event association rules, which utilizes the computational resource of multiple dedicated nodes of the system. Experimental results show that our decentralized event correlation mining algorithm achieves nearly ideal speedup compared to centralized mining approaches.     

基于贝叶斯分类的选股方法

本文使用“事件研究”方法分析了证券分析师推荐股票的总体特征,试图找出符合这些特征的股票而获得超额回报,并应用基本贝叶斯分类方法进行选股。经对上证A股的所选股票的收益率统计分析,通过合理地选取贝叶斯分类器参数可以获得较好回报。结果表明了这种方法是有实际意义和效果的。     

System-on-chip design by proof-based refinement

Systems-on-chip (SoCs) and SoC architectures provide a collection of challenging problems related to specification, modelling techniques, security issues and structuring questions. We describe a design methodology integrating the event B method and characterized by the incremental and proof-controlled construction of SoC models. The essence of the methodology is the refinement of models, starting from system requirements and producing event B models for characterizing the system under development. The refinement is a unifying concept that ensures the consistency of the different models produced and our contribution is an illustration through a case study, namely a system for measuring the parameters of audio/video quality in the digital video broadcasting (DVB) set of digital TV standards. The first part is the derivation of an architecture of parameters from the document ETSI TR 101 290 and the validation of the architecture using invariants of B models. The second part is the proposal of B models of the SystemC scheduler and an instantiation of these abstract models of the simulation semantics by parameters of the SystemC codes automatically translated from the B models of the DVB system. Finally, the third part relies upon a proof-based methodology for deriving an operational semantics of a given system that is expressed by an event B model including invariant properties.     

可定制的安全操作系统内核

随着因特网的高速发展,安全问题显得日益严重,而计算机操作系统是信息系统的安全基础,但传统的操作系统安全机制已缺乏足够的灵活性,不能提供可根据具体应用系统配置特定安全策略的机制,提出了一种可定制的安全操作系统内核机制,用户可使用基于规则,面向事件的高层定义语言来描述安全策略而无需关心底层细节,系统具有良好的适应能力,可支持广泛的安全策略的实现。     

通用事件服务规范的设计与实现

在CORBA标准中,ORB提供了一种同步通信的机制。但是,许多应用需要进行异步通信。OMG为此定义了事件服务。本文提出了事件服务的一种多线程设计和实现方案。该实现方案在应用中显示了很好的性能。     

离散事件动态系统仿真工具DEDS Sim及其应用研究

该文叙述了离散事件动态系统仿真工具DEDS Sim的设计方法和设计结构。DEDS Sim基于面向对象方法,运用COM（Component Object Model)组件技术,采用图形窗口环境,通过拖放方式组态系统,联线方式建立模型间逻辑联系,并具备系统扩展功能。该仿真工具已经在某一炼钢连铸物流系统得到了成功地运用。     

事件驱动RFID系统开发及其应用

随着RFID硬件技术的不断成熟和生产成本的不断降低,RFID已广泛应用于零售超市、物流与供应链管理、图书管理、设备与资产管理等实时监控和追踪领域。本文主要研究事件驱动的RFID系统开发方法,该方法通过将大量数据表示成不同类型事件（包括原始事件和复杂事件）,设计了事件驱动的RFID事件处理框架。基于此框架,研究了基于Hash表事件过滤机制和复杂事件匹配方法,以实现对RFID事件流的实时分析与处理。最后,结合一个RFID模拟零售超市管理系统,验证了方法的可行性和有效性。     

基于EFPN的模糊离散事件系统可诊断性分析

针对模糊有限自动机及模糊Petri网在模糊离散事件系统(Fuzzy Discrete Event System,FDES)行为建模中的不足,提出一种扩展模糊Petri网(Extended Fuzzy Petri Net,EFPN),并基于EFPN构造了FDES行为模型及系统故障诊断器。EFPN以一种新的事件模型为基础对FDES的系统行为进行建模,并根据事件触发矩阵计算下一系统状态中各分量的可能性分布。与模糊有限自动机及模糊Petri网的对比分析表明,在描述FDES模糊事件时,对于同步距离为0的一组事件所造成的影响,EFPN的表达能力强于经典模糊Petri网;在对含有多个状态分量的FDES建模时,EFPN模型的规模远小于模糊有限自动机。在基于EFPN构造的系统行为模型中,运用可达状态标识图分析系统状态集,可有效地构造FDES的故障诊断器,并可据此进行系统可诊断性分析。     

一种WSN中的三层多维事件协作检测算法

提出了一种适用于无线传感器网络的三层多维事件协作检测算法。传感器节点通过计算均值向量序列的相似度发现异常,并通过投票机制确认事件发生。簇头节点根据边界向量序列的相似度,利用改进的K均值聚类算法对多维事件数据进行分类和合并。汇聚节点利用事件属性数据的概率分布,匹配检测出事件的类型。理论分析和仿真试验的结果表明:与传统集中式的事件检测算法相比,该算法能在噪声干扰下提高对多维事件的检测精度,降低算法的通信量和计算复杂度,延长网络的生存时间。