本文選題：精準(zhǔn)決策支持 + 大數(shù)據(jù)支持平臺(tái)��； 參考：《北京交通大學(xué)》2017年碩士論文

【摘要】：對(duì)于互聯(lián)網(wǎng)產(chǎn)品來講,精準(zhǔn)的決策和運(yùn)營(yíng)決定著產(chǎn)品的興盛與衰亡。目前,互聯(lián)網(wǎng)產(chǎn)品的決策和運(yùn)營(yíng)主要依靠于產(chǎn)品團(tuán)隊(duì)和運(yùn)營(yíng)團(tuán)隊(duì)人員的經(jīng)驗(yàn),但是真正可以做到精準(zhǔn)決策的人才有如鳳毛麟角。傳統(tǒng)的運(yùn)營(yíng)手段,不僅燒錢而且存在著用戶粘性低等問題。網(wǎng)易作為互聯(lián)網(wǎng)的先行者,各個(gè)業(yè)務(wù)線都擁有豐富的數(shù)據(jù),但是各業(yè)務(wù)線的數(shù)據(jù)分析工作零散,適用范圍狹窄,缺乏通用性。因此公司迫切需要一個(gè)平臺(tái)整合內(nèi)部的數(shù)據(jù)資源,為相關(guān)部門統(tǒng)一提供精準(zhǔn)的數(shù)據(jù)分析支持服務(wù)。在這一背景下,移動(dòng)大數(shù)據(jù)支持平臺(tái)應(yīng)運(yùn)而生,為克服傳統(tǒng)決策和運(yùn)營(yíng)手段的局限性、提高公司資源復(fù)用提供了良好的解決方案。本平臺(tái)基于分布式開發(fā)組件開發(fā),所運(yùn)用到的組件有Kafka、HDFS、Spark、Hive、HDFS Connector 等,同時(shí)結(jié)合 Spring、MyBatis 等 Java Web 框架技術(shù)進(jìn)行系統(tǒng)后臺(tái)服務(wù)的支持。平臺(tái)由基礎(chǔ)架構(gòu)子系統(tǒng)、數(shù)據(jù)分析子系統(tǒng)和數(shù)據(jù)挖掘子系統(tǒng)組成。實(shí)習(xí)過程中,本人主要負(fù)責(zé)數(shù)據(jù)分析子系統(tǒng)的設(shè)計(jì)與開發(fā)工作,主要包括用戶行為分析、交易行為分析、頁(yè)面熱力圖、路徑分析和輔助服務(wù)5個(gè)模塊。其中,前4個(gè)模塊的功能實(shí)現(xiàn)由數(shù)據(jù)分析部分和后臺(tái)服務(wù)部分協(xié)作完成,輔助服務(wù)模塊的功能實(shí)現(xiàn)由后臺(tái)服務(wù)部分獨(dú)立完成。數(shù)據(jù)分析部分使用大數(shù)據(jù)分析技術(shù),后臺(tái)服務(wù)部分使用Java Web技術(shù)。本論文中首先介紹平臺(tái)開發(fā)過程中所用到的關(guān)鍵性技術(shù),其次從整體上對(duì)平臺(tái)的功能進(jìn)行介紹,著重分析了本人負(fù)責(zé)模塊的功能需求,并對(duì)系統(tǒng)的非功能性需求進(jìn)行了闡述。然后介紹了系統(tǒng)的技術(shù)架構(gòu)設(shè)計(jì)、功能架構(gòu)設(shè)計(jì)和數(shù)據(jù)庫(kù)設(shè)計(jì)。接著重點(diǎn)介紹了平臺(tái)的詳細(xì)設(shè)計(jì)與實(shí)現(xiàn)過程,并展示了平臺(tái)各模塊的實(shí)現(xiàn)效果圖。最后對(duì)平臺(tái)使用的測(cè)試與驗(yàn)證方法進(jìn)行了說明。移動(dòng)大數(shù)據(jù)支持平臺(tái)為網(wǎng)易旗下的眾多產(chǎn)品提供了簡(jiǎn)潔方便的產(chǎn)品接入點(diǎn)和精準(zhǔn)的數(shù)據(jù)分析支持服務(wù),提高了公司內(nèi)部數(shù)據(jù)資源的復(fù)用性,提升了產(chǎn)品、運(yùn)營(yíng)等相關(guān)部門的工作效率。同時(shí),為數(shù)據(jù)挖掘團(tuán)隊(duì)提供數(shù)據(jù)源,幫助他們進(jìn)一步挖掘數(shù)據(jù)價(jià)值,提高部門和公司的收益。
[Abstract]:For Internet products, accurate decision-making and operation determine the prosperity and decline of products. At present, the decision and operation of Internet products mainly depend on the experience of product team and operation team, but few people can make accurate decision. Traditional operation means, not only burn money but also the problem of low user viscosity. As the forerunner of the Internet, NetEase has abundant data for each service line, but the data analysis work of each service line is scattered, the scope of application is narrow, and lack of generality. Therefore, the company urgently needs a platform to integrate internal data resources and provide accurate data analysis support services for relevant departments. Under this background, the mobile big data support platform emerges as the times require, which provides a good solution for overcoming the limitation of traditional decision-making and operation means and improving the reuse of company resources. This platform is based on distributed development component development. The components used in this platform are Kafka HDFSX / SparkConnector and so on. At the same time, the system background service is supported by combining with Spring's MyBatis and other Java Web framework technologies. The platform consists of infrastructure subsystem, data analysis subsystem and data mining subsystem. In the process of practice, I am mainly responsible for the design and development of the data analysis subsystem, including user behavior analysis, transaction behavior analysis, thermal diagram of the page, path analysis and auxiliary service five modules. Among them, the first four modules are implemented by the data analysis part and the background service part, and the auxiliary service module is implemented independently by the background service part. Data analysis part uses big data analysis technology, background service part uses Java Web technology. This paper first introduces the key technologies used in the development of the platform, then introduces the functions of the platform as a whole, analyzes the functional requirements of the responsible module, and expounds the non-functional requirements of the system. Then introduces the system's technical architecture design, functional architecture design and database design. Then the detailed design and implementation process of the platform are introduced, and the implementation effect of each module is shown. Finally, the test and verification methods used in the platform are described. Mobile big data support platform provides simple and convenient product access points and accurate data analysis support services for many products under NetEase, improves the reuse of data resources within the company, and improves the products. Work efficiency of related departments such as operation. At the same time, provide data sources for data mining teams, help them to further mining data value, and improve the revenue of departments and companies.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.52;TP311.13

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