基于CEP并行化處理的犯罪線索實(shí)時(shí)推薦研究
發(fā)布時(shí)間：2019-05-19 00:02
【摘要】：縱觀歷史,犯罪從國(guó)家產(chǎn)生之日就與之相伴隨,存在于各個(gè)歷史階段和各種社會(huì)類型,從未消失。網(wǎng)絡(luò)技術(shù)的誕生和發(fā)展,為案件偵查提供更加廣闊的途徑。依靠網(wǎng)絡(luò)開展犯罪線索搜集將成為案件偵查的新方法。隨著互聯(lián)網(wǎng)技術(shù)的高速發(fā)展,網(wǎng)絡(luò)的信息數(shù)據(jù)已經(jīng)呈指數(shù)增長(zhǎng),因此如何從海量的數(shù)據(jù)中快速的查找有價(jià)值的線索并實(shí)時(shí)地推薦給相關(guān)部門是案件偵查亟需解決的問題。復(fù)雜事件處理(Complex Event Processing,CEP)是將外部源源不斷的數(shù)據(jù)流抽象為事件,再通過對(duì)事件的過濾、聚合等操作,檢測(cè)事先定義好的復(fù)雜事件模式并對(duì)其進(jìn)行響應(yīng)的過程。它最大的優(yōu)勢(shì)是處理實(shí)時(shí)數(shù)據(jù),這能讓我們?cè)谧疃痰臅r(shí)間內(nèi)獲取最有用的信息。復(fù)雜事件處理的并行化有效的解決了集中式處理存在的性能問題,在面對(duì)海量數(shù)據(jù)時(shí)表現(xiàn)出極大的優(yōu)勢(shì)。在原有犯罪線索收集的基礎(chǔ)上,引入并行化的復(fù)雜事件處理技術(shù),為相關(guān)部門推薦犯罪線索提供實(shí)時(shí)性和高效性的保證。本文通過對(duì)復(fù)雜事件處理并行化技術(shù)進(jìn)行研究,在現(xiàn)有的網(wǎng)絡(luò)犯罪線索收集系統(tǒng)的基礎(chǔ)上,結(jié)合復(fù)雜事件處理技術(shù)在實(shí)時(shí)處理方面的優(yōu)勢(shì),開展基于CEP并行化處理的犯罪線索實(shí)時(shí)推薦的研究和系統(tǒng)的實(shí)現(xiàn)。通過對(duì)職務(wù)犯罪類型之間的關(guān)系進(jìn)行分析,構(gòu)建了面向職務(wù)犯罪的關(guān)鍵詞語(yǔ)義樹,節(jié)點(diǎn)所在的層數(shù)作為節(jié)點(diǎn)的權(quán)值。本文根據(jù)關(guān)鍵詞的語(yǔ)義關(guān)系來(lái)計(jì)算匹配的關(guān)鍵詞個(gè)數(shù)和總權(quán)值,推薦規(guī)則是總權(quán)值的大小進(jìn)行推薦。在模式分析的基礎(chǔ)上,給出了模式研究與設(shè)計(jì)過程中的一些關(guān)鍵步驟,并針對(duì)系統(tǒng)的核心模塊進(jìn)行詳細(xì)的設(shè)計(jì)與實(shí)現(xiàn)。經(jīng)過實(shí)驗(yàn)結(jié)果對(duì)比分析,該方法比傳統(tǒng)方法的準(zhǔn)確率要高。在海量數(shù)據(jù)和系統(tǒng)實(shí)時(shí)性要求比較高時(shí),該系統(tǒng)具有一定的優(yōu)勢(shì)。
[Abstract]:Throughout history, crime has been accompanied by it from the date of the emergence of the country, existing in various historical stages and various social types, and has never disappeared. The birth and development of network technology provides a broader way for case investigation. Relying on the network to collect criminal clues will become a new method of case investigation. With the rapid development of Internet technology, the information data of the network has shown an exponential growth, so how to quickly find valuable clues from the massive data and recommend it to the relevant departments in real time is an urgent problem to be solved in case investigation. Complex event processing (Complex Event Processing,CEP) is the process of abstracting the external flow of data into events, and then detecting and responding to the pre-defined complex event patterns through the filtering and aggregation of events. Its biggest advantage is to process real-time data, which allows us to get the most useful information in the shortest possible time. The parallelization of complex event processing effectively solves the performance problems of centralized processing and shows great advantages in the face of massive data. On the basis of the collection of the original crime clues, the parallel complex event processing technology is introduced to provide the real-time and efficient guarantee for the relevant departments to recommend the crime clues. In this paper, through the research of complex event processing parallelization technology, on the basis of the existing network crime clue collection system, combined with the advantages of complex event processing technology in real-time processing, The research and system implementation of real-time recommendation of crime clues based on CEP parallel processing are carried out. Based on the analysis of the relationship between the types of job-related crimes, a keyword semantic tree for job-related crimes is constructed, and the number of layers in which the nodes are located is taken as the weight of the nodes. In this paper, the number and total weight of matching keywords are calculated according to the semantic relationship of keywords, and the recommendation rule is the size of total weights to recommend. On the basis of pattern analysis, some key steps in the process of pattern research and design are given, and the core modules of the system are designed and implemented in detail. The experimental results show that the accuracy of this method is higher than that of the traditional method. When the real-time requirements of massive data and system are relatively high, the system has certain advantages.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：D917
本文編號(hào)：2480453
[Abstract]:Throughout history, crime has been accompanied by it from the date of the emergence of the country, existing in various historical stages and various social types, and has never disappeared. The birth and development of network technology provides a broader way for case investigation. Relying on the network to collect criminal clues will become a new method of case investigation. With the rapid development of Internet technology, the information data of the network has shown an exponential growth, so how to quickly find valuable clues from the massive data and recommend it to the relevant departments in real time is an urgent problem to be solved in case investigation. Complex event processing (Complex Event Processing,CEP) is the process of abstracting the external flow of data into events, and then detecting and responding to the pre-defined complex event patterns through the filtering and aggregation of events. Its biggest advantage is to process real-time data, which allows us to get the most useful information in the shortest possible time. The parallelization of complex event processing effectively solves the performance problems of centralized processing and shows great advantages in the face of massive data. On the basis of the collection of the original crime clues, the parallel complex event processing technology is introduced to provide the real-time and efficient guarantee for the relevant departments to recommend the crime clues. In this paper, through the research of complex event processing parallelization technology, on the basis of the existing network crime clue collection system, combined with the advantages of complex event processing technology in real-time processing, The research and system implementation of real-time recommendation of crime clues based on CEP parallel processing are carried out. Based on the analysis of the relationship between the types of job-related crimes, a keyword semantic tree for job-related crimes is constructed, and the number of layers in which the nodes are located is taken as the weight of the nodes. In this paper, the number and total weight of matching keywords are calculated according to the semantic relationship of keywords, and the recommendation rule is the size of total weights to recommend. On the basis of pattern analysis, some key steps in the process of pattern research and design are given, and the core modules of the system are designed and implemented in detail. The experimental results show that the accuracy of this method is higher than that of the traditional method. When the real-time requirements of massive data and system are relatively high, the system has certain advantages.
【學(xué)位授予單位】：華北電力大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：D917
【引證文獻(xiàn)】
相關(guān)碩士學(xué)位論文 前1條
1 萬(wàn)傲青;基于大眾點(diǎn)評(píng)網(wǎng)數(shù)據(jù)的武漢中心城區(qū)餐飲消費(fèi)空間分布研究[D];湖北大學(xué);2018年
，本文編號(hào)：2480453
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