本文選題：復(fù)雜事件處理 + 流式計(jì)算　； 參考：《北京工業(yè)大學(xué)》2016年碩士論文

【摘要】：隨著云計(jì)算時(shí)代的到來(lái),很多研究人員在近年來(lái)開(kāi)始關(guān)注大數(shù)據(jù)領(lǐng)域。但是大數(shù)據(jù)在發(fā)展的過(guò)程中還存在著很多的挑戰(zhàn),比如如何利用信息技術(shù)等手段處理XML等半結(jié)構(gòu)化數(shù)據(jù)、數(shù)據(jù)的表示和轉(zhuǎn)化、如何高效地處理這些數(shù)據(jù)、適合不同行業(yè)應(yīng)用的開(kāi)發(fā)環(huán)境等等。復(fù)雜事件處理是在事件驅(qū)動(dòng)架構(gòu)下,結(jié)合簡(jiǎn)單事件、事件流處理以及復(fù)合事件進(jìn)行處理,是大數(shù)據(jù)處理的關(guān)鍵技術(shù)之一。它通過(guò)提取符合特定模式的事件序列并對(duì)其進(jìn)行實(shí)時(shí)檢測(cè),能夠滿足海量數(shù)據(jù)處理中高吞吐量、低延遲的需求。目前有許多研究人員提出了各種復(fù)雜事件流處理語(yǔ)言和流式計(jì)算平臺(tái),但都有自己的局限性。針對(duì)現(xiàn)有的復(fù)雜事件處理引擎存在的各種問(wèn)題,本文提出并設(shè)計(jì)了一個(gè)以復(fù)雜事件流處理語(yǔ)言CEStream為基礎(chǔ)的分布式復(fù)雜事件流處理引擎,實(shí)現(xiàn)了基于正規(guī)樹(shù)模式的事件檢測(cè)功能,能夠同時(shí)支持時(shí)間序列的正規(guī)式匹配和半結(jié)構(gòu)化數(shù)據(jù)的結(jié)構(gòu)約束,并且可以捕獲來(lái)自不同事件源的數(shù)據(jù),檢測(cè)符合特定時(shí)間序列的正規(guī)式模式的組合型事件。針對(duì)多數(shù)據(jù)源組合型事件的檢測(cè)需求,本文還提出了一個(gè)模式分解算法,可以將復(fù)雜事件處理任務(wù)分解為多個(gè)獨(dú)立的事件檢測(cè)任務(wù),部署在集群中不同節(jié)點(diǎn)和遠(yuǎn)端的事件檢測(cè)代理上,從而減少單源數(shù)據(jù)的傳輸消耗,并通過(guò)集群的并行計(jì)算功能提高多源事件檢測(cè)效率。實(shí)驗(yàn)結(jié)果表明,系統(tǒng)實(shí)現(xiàn)了CEStream語(yǔ)言的查詢功能,能夠完成正規(guī)樹(shù)模式匹配和多源組合型事件檢測(cè)等特色功能,并通過(guò)模式分解算法有效提高了系統(tǒng)的事件檢測(cè)效率,達(dá)到了低延時(shí)和高吞吐量的設(shè)計(jì)目標(biāo),可以滿足目前主流的復(fù)雜事件流處理的應(yīng)用場(chǎng)景。
[Abstract]:With the arrival of cloud computing era, many researchers began to pay attention to big data field in recent years. However, big data still has many challenges in the process of development, such as how to use information technology and other means to deal with semi-structured data such as XML, how to represent and transform data, how to deal with these data efficiently. Suitable for different industry application development environment and so on. Complex event processing is one of the key techniques for big data to deal with simple events, event flow and composite events under the event-driven architecture. It can meet the requirements of high throughput and low latency in mass data processing by extracting and detecting event sequences according to specific patterns. At present, many researchers have proposed a variety of complex event flow processing languages and flow computing platforms, but they all have their own limitations. Aiming at the various problems existing in the existing complex event processing engine, this paper proposes and designs a distributed complex event flow processing engine based on the complex event flow processing language CEStream, and realizes the event detection function based on the normal tree pattern. It can support both the normal matching of time series and the structural constraints of semi-structured data, and can capture data from different event sources and detect combinational events with normal pattern of specific time series. Aiming at the detection requirements of multi-data source composite events, this paper also proposes a schema decomposition algorithm, which can decompose complex event processing tasks into multiple independent event detection tasks. It is deployed on different nodes and remote event detection agents in the cluster to reduce the transmission cost of single source data and to improve the efficiency of multi-source event detection through the parallel computing function of the cluster. The experimental results show that the system realizes the query function of CEStream language, can complete the characteristic functions such as regular tree pattern matching and multi-source combinational event detection, and effectively improves the efficiency of event detection through pattern decomposition algorithm. The design goal of low delay and high throughput is achieved, which can meet the application scenarios of complex event flow processing.
【學(xué)位授予單位】：北京工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TP311.13

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