本文選題：移動(dòng)互聯(lián)網(wǎng) + Hadoop　； 參考：《北京郵電大學(xué)》2014年碩士論文

【摘要】：隨著互聯(lián)網(wǎng)的飛速發(fā)展,網(wǎng)絡(luò)規(guī)模的不斷擴(kuò)展,互聯(lián)網(wǎng)已經(jīng)深入到人們的方方面面的生活中�，F(xiàn)如今,隨著移動(dòng)終端的廣泛普及,移動(dòng)互聯(lián)網(wǎng)已經(jīng)成為人們生活中必不可少的一部分。在人們使用各種移動(dòng)互聯(lián)網(wǎng)業(yè)務(wù)的同時(shí),在與移動(dòng)網(wǎng)絡(luò)以及網(wǎng)絡(luò)中用戶(hù)的交互過(guò)程中,產(chǎn)生了各式各樣的網(wǎng)絡(luò)行為。如果說(shuō)前些年互聯(lián)網(wǎng)給人們帶來(lái)各種各樣多姿多彩的信息,那么近幾年飛速發(fā)展的移動(dòng)互聯(lián)網(wǎng)則使人們更加方便和簡(jiǎn)單地接入互聯(lián)網(wǎng),能夠隨心所欲地在任何時(shí)間地點(diǎn)接入互聯(lián)網(wǎng),更加簡(jiǎn)單快捷地獲取到各種信息。 本文分析了移動(dòng)互聯(lián)網(wǎng)用戶(hù)行為分析的意義,介紹了移動(dòng)互聯(lián)網(wǎng)的邏輯架構(gòu)以及移動(dòng)互聯(lián)網(wǎng)流量分析的特點(diǎn)等。接下來(lái),介紹了用戶(hù)行為分析的概念等,并闡述了海量數(shù)據(jù)處理的基礎(chǔ)知識(shí)、難點(diǎn)與處理技巧,數(shù)據(jù)挖掘的目的、步驟和方法。接下來(lái)詳細(xì)介紹了小區(qū)流量的統(tǒng)計(jì)方法,并且對(duì)各個(gè)統(tǒng)計(jì)方法所得到的結(jié)果的相對(duì)誤差做了詳細(xì)的對(duì)比分析,并且計(jì)算了各個(gè)方法的時(shí)間復(fù)雜度,對(duì)各個(gè)方法的實(shí)際運(yùn)行時(shí)間做了統(tǒng)計(jì)以及對(duì)比分析。針對(duì)Gn接口數(shù)據(jù)存在小區(qū)重選的誤差對(duì)小區(qū)流量統(tǒng)計(jì)的影響,本文首先對(duì)Gb接口數(shù)據(jù)與Gn接口數(shù)據(jù)的流基本特征做了詳細(xì)的統(tǒng)計(jì)和比較,再根據(jù)Gb接口數(shù)據(jù)統(tǒng)計(jì)小區(qū)重選的情況,以及相關(guān)分析。以確定小區(qū)重選對(duì)Gn接口數(shù)據(jù)所得到的小區(qū)流量統(tǒng)計(jì)的影響。接下來(lái),從多個(gè)維度對(duì)小區(qū)流量與時(shí)間相關(guān)性進(jìn)行分析。并且運(yùn)用數(shù)據(jù)挖掘知識(shí),K-Means算法基于流量對(duì)小區(qū)進(jìn)行聚類(lèi)分析,并對(duì)分類(lèi)后的各組表現(xiàn)出的特征進(jìn)行深入分析和對(duì)比。
[Abstract]:With the rapid development of the Internet and the continuous expansion of the network scale, the Internet has penetrated into all aspects of people's lives. Nowadays, with the popularity of mobile terminals, mobile Internet has become an indispensable part of people's lives. When people use all kinds of mobile Internet services, they produce a variety of network behaviors in the process of interaction with mobile networks and users in the network. If the Internet brought people a variety of colorful information in the past few years, the rapid development of the mobile Internet in recent years has made it more convenient and simple for people to access the Internet. Access to the Internet anytime and anywhere, easier and faster access to all kinds of information. This paper analyzes the significance of mobile Internet user behavior analysis, introduces the logical structure of mobile Internet and the characteristics of mobile Internet traffic analysis. Then, the concept of user behavior analysis is introduced, and the basic knowledge, difficulties and processing skills of mass data processing, the purpose, steps and methods of data mining are expounded. Then the statistical methods of cell flow are introduced in detail, and the relative errors of the results obtained by each statistical method are compared and analyzed in detail, and the time complexity of each method is calculated. The actual running time of each method is statistically analyzed and compared. In view of the effect of the error of cell reselection in Gn interface data on cell traffic statistics, this paper firstly makes detailed statistics and comparison on the basic characteristics of Gb interface data and Gn interface data. Then according to GB interface data statistics cell reselection, as well as related analysis. The effect of cell reselection on cell traffic statistics obtained from Gn interface data is determined. Then, the correlation between cell traffic and time is analyzed from multiple dimensions. The K-Means algorithm of data mining is used to analyze the cluster of the cells based on the traffic, and the characteristics of each group after classification are analyzed and compared in depth.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP393.01;TN929.5

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本文編號(hào)：1826917