本文選題：用戶聚類 + 關(guān)鍵質(zhì)量指標(biāo)　； 參考：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年博士論文

【摘要】：傳統(tǒng)的基于關(guān)鍵性能指標(biāo)(KPI:Key Performance Indicator)和用戶投訴的體驗(yàn)運(yùn)維方式已暴露諸多弊端,如:發(fā)現(xiàn)問題具有被動(dòng)性、局限性,解決問題精細(xì)化程度不足等。對(duì)于電信運(yùn)營(yíng)商來說,探索面向智能化運(yùn)維的精細(xì)化客戶體驗(yàn)管理是當(dāng)前及未來的重要研究領(lǐng)域。面向客戶體驗(yàn)生命周期中體驗(yàn)前、中、后三大環(huán)節(jié),智能化的客戶體驗(yàn)管理應(yīng)具備如下能力:在業(yè)務(wù)發(fā)生前進(jìn)行個(gè)性化用戶偏好需求分析與預(yù)判,制定事前預(yù)防策略;在業(yè)務(wù)發(fā)生過程中快速發(fā)現(xiàn)體驗(yàn)問題、定位/定界問題并動(dòng)態(tài)實(shí)時(shí)調(diào)整;在業(yè)務(wù)結(jié)束后基于歷史數(shù)據(jù)學(xué)習(xí)進(jìn)一步迭代優(yōu)化網(wǎng)絡(luò),最終形成智能運(yùn)維的閉環(huán)。本文的研究問題聚焦在體驗(yàn)前用戶的個(gè)性化偏好行為建模、體驗(yàn)中的準(zhǔn)確監(jiān)測(cè)體驗(yàn)問題和定界問題的相關(guān)策略。得益于大數(shù)據(jù)技術(shù)的快速發(fā)展,電信運(yùn)營(yíng)商逐步具備了海量用戶數(shù)據(jù)采集、存儲(chǔ)和計(jì)算的條件和能力,上述問題得以廣泛研究,但仍存在諸多挑戰(zhàn),如:數(shù)據(jù)規(guī)模巨大,體驗(yàn)管理復(fù)雜度極高;數(shù)據(jù)質(zhì)量不高,體驗(yàn)信息難以準(zhǔn)確刻畫;體驗(yàn)異常成因復(fù)雜。針對(duì)上述挑戰(zhàn),本文具體研究?jī)?nèi)容包括:(1)移動(dòng)用戶的業(yè)務(wù)行為偏好建模,該研究針對(duì)大規(guī)模用戶的精細(xì)化體驗(yàn)管理面臨的實(shí)現(xiàn)復(fù)雜度超高的問題,提出了基于于聚類的用戶行為偏好建模方式,平衡精細(xì)化的度和體驗(yàn)管理的復(fù)雜度。針對(duì)Kmeans聚類方法存在的聚類速度慢問題,提出的AFKmc2+兩階段聚類算法:首先利用自組織映射思想將原始數(shù)據(jù)映射為數(shù)據(jù)原型,然后借助馬爾科夫蒙特卡洛(MCMC:Markov Chain Monte Carlo)采樣理論快速、合理選擇原型初始聚類中心,最終利用Kmeans完成快速聚類。針對(duì)基于雙側(cè)正交約束的非負(fù)矩陣分解(tNMF:Tri-Non-negative Matrix Factorization)的雙向聚類算法存在聚類個(gè)數(shù)事先未知、頻繁大矩陣相乘及硬聚類策略導(dǎo)致聚類效果差問題,提出的H-tNMF算法:借助密度聚類思想實(shí)現(xiàn)聚類維度可伸縮,減少頻繁大矩陣相乘操作;引入分裂層次聚類思想自動(dòng)確定聚類個(gè)數(shù);定義雙向簇緊致度概念,實(shí)現(xiàn)子簇維度共享軟聚類,避免錯(cuò)誤累積。利用公開數(shù)據(jù)集以及現(xiàn)網(wǎng)用戶數(shù)據(jù)集驗(yàn)證所提算法性能,實(shí)驗(yàn)結(jié)果證明:相比于AFKmc2算法,AFKmc2+在具有更快的聚類速度的同時(shí)具有相接近的聚類精度;相比于tNMF算法,H-tNMF在相同聚類數(shù)目下具有更好的聚類效果,且支持軟聚類。(2)網(wǎng)絡(luò)側(cè)視頻點(diǎn)播業(yè)務(wù)卡頓識(shí)別,該研究針對(duì)采集條件制約導(dǎo)致的視頻傳輸狀態(tài)數(shù)據(jù)質(zhì)量不高問題,提出基于特征構(gòu)造結(jié)合黑箱策略的卡頓建模方法,方法克服數(shù)據(jù)中存在的偏差,可以準(zhǔn)確識(shí)別出視頻卡頓、長(zhǎng)卡頓和多次卡頓。通過分析導(dǎo)致現(xiàn)有卡頓識(shí)別方法準(zhǔn)確率低的"時(shí)間漂移"現(xiàn)象,提出一種不進(jìn)行視頻重建,而只關(guān)注如何構(gòu)建網(wǎng)絡(luò)側(cè)視頻數(shù)據(jù)流和終端側(cè)視頻卡頓間的映射關(guān)系的黑箱策略。通過剖析視頻卡頓機(jī)理,提出一種基于網(wǎng)絡(luò)狀態(tài)局部極差值的緩沖區(qū)剩余數(shù)據(jù)量估計(jì)方法(Freeze Divine)用于映射關(guān)系學(xué)習(xí)。所提方法將現(xiàn)有卡頓識(shí)別方案中的"重構(gòu)+硬判決"的識(shí)別思路轉(zhuǎn)變?yōu)?估計(jì)+數(shù)據(jù)驅(qū)動(dòng)下的軟判決"。通過構(gòu)造現(xiàn)網(wǎng)視頻卡頓數(shù)據(jù)集驗(yàn)證所提算法性能,實(shí)驗(yàn)結(jié)果證明:所提方法的卡頓識(shí)別準(zhǔn)確率比現(xiàn)有方法高20%;Freeze Divine所提去的卡頓相關(guān)特征較其他特征與視頻卡頓的相關(guān)性更強(qiáng)。(3)網(wǎng)絡(luò)側(cè) HTTP 業(yè)務(wù)響應(yīng)延遲(spRTT:Service Provider Round Trip Time)異常根因定界,該研究針對(duì)端到端業(yè)務(wù)體驗(yàn)異常成因不單一問題,提出基于多元線性回歸的分治場(chǎng)景建模方法(DC-CoMo:Divide and Conquer based Context Modeling)和基于相對(duì)摘的貪婪搜索樹構(gòu)建方法(ReasonTree),高效標(biāo)定異常,準(zhǔn)確定界異常根因。提出DC-CoMo算法:利用層次維度聚合思想降低spRTT時(shí)序稀疏性;利用分治思想進(jìn)行海量spRTT時(shí)序建模,權(quán)衡spRTT場(chǎng)景模型的復(fù)雜度和準(zhǔn)確度;建模采用多元線性回歸方法,綜合考慮異常點(diǎn)及場(chǎng)景對(duì)spRTT的綜合影響。針對(duì)現(xiàn)有業(yè)務(wù)KQI異常定界方法難以同時(shí)定界小范圍異常根因、多維異常根因和多類型并存的異常根因問題,提出ReasonTree算法:算法首先利用相對(duì)熵量化場(chǎng)景屬性對(duì)異常的區(qū)分度,定界小范圍異常;通過構(gòu)建搜索樹的方式計(jì)算不同場(chǎng)景屬性組合的異常得分,定界多維異常;利用貪婪迭代策略,識(shí)別異常主導(dǎo)原因并過濾對(duì)應(yīng)數(shù)據(jù),定界多類型并存異常;ReasonTree不基于歷史定界結(jié)果,能夠發(fā)現(xiàn)新的異常根因。利用現(xiàn)網(wǎng)HTTP業(yè)務(wù)spRTT數(shù)據(jù)集及人工注入異常數(shù)據(jù)集驗(yàn)證所提算法性能,實(shí)驗(yàn)結(jié)果表明:對(duì)于人工注入的三種異常根因,所提ReasonTree方法均具有超過95%的異常根因定界準(zhǔn)確率;結(jié)合ReasonTree算法,DC-CoMo算法利用少量模型便可定界傳統(tǒng)逐一建模算法約93%的異常根因。本文以自主研發(fā)的xDR-Pro和KQI-Doctor平臺(tái)為基礎(chǔ),以現(xiàn)網(wǎng)多點(diǎn)實(shí)測(cè)的用戶業(yè)務(wù)數(shù)據(jù)為支撐,通過對(duì)現(xiàn)有聚類、分類和異常檢測(cè)等算法的使用和改進(jìn),研究了面向智能運(yùn)維的精細(xì)化客戶體驗(yàn)管理中的移動(dòng)用戶偏好建模、網(wǎng)絡(luò)側(cè)視頻點(diǎn)播業(yè)務(wù)卡頓識(shí)別以及SP響應(yīng)延遲異常定界問題。所提方法和策略為面向智能運(yùn)維的精細(xì)化客戶體驗(yàn)管理提供新思路。
[Abstract]:The traditional way of experience and maintenance based on KPI:Key Performance Indicator and user complaint has exposed many disadvantages, such as: finding the problem with passivity, limitation, and the lack of fine resolution. For the telecom operators, the exploration of the refined customer experience management for the intelligent operation and maintenance is the current and An important research field in the future. In the three major links of experience, in the customer experience life cycle, and in the post experience, the intelligent customer experience management should have the following ability: to analyze and prejudge the needs of personalized user preferences before the occurrence of business, and to formulate pre event prevention strategies; to find the experience problem quickly in the process of business affairs, and to locate / delimit the boundary. The problem and dynamic real-time adjustment; after the end of the business, further iterative optimization network based on historical data learning, and finally the closed loop of intelligent operation and maintenance is formed. The research problem of this paper focuses on the personalized preference behavior modeling of the user before experience, and the related strategies of accurate monitoring experience and delimiting problems in the experience. The rapid development of the telecom operators has gradually acquired the conditions and capabilities of mass user data collection, storage and computing. The above problems have been widely studied, but there are still many challenges, such as: the large scale of the data, the high complexity of experience management, the poor quality of the data, the difficult to accurately depict the experience information, and the complicated causes of the experience of the abnormity. The specific research contents of this paper include: (1) the business behavior preference modeling of mobile users. This study aims at the complexity of the sophisticated experience management of large-scale users, and proposes a clustering based user behavior preference modeling method, balancing the degree of refinement and the complexity of experience management. The AFKmc2+ two stage clustering algorithm proposed by the class method is slow. First, the original data is mapped to the data prototype using the self organizing mapping idea, and then the initial cluster center of the prototype is selected reasonably with the help of Markoff Montecarlo (MCMC:Markov Chain Monte Carlo) sampling theory. Finally, the Kmeans is used to complete the rapid completion of the cluster. For the bidirectional clustering algorithm based on tNMF:Tri-Non-negative Matrix Factorization based on bilateral orthogonal constraints, the number of clustering numbers is unknown in advance, the frequent large matrix multiplication and the hard clustering strategy lead to the poor clustering effect. The H-tNMF algorithm is proposed to achieve the scalability of the clustering dimension with the aid of the density clustering idea. Reduce the multiplicative operation of frequent large matrix, introduce the split hierarchical clustering idea to determine the number of clustering automatically, define the concept of two-way cluster tightness, realize the subcluster dimension sharing soft clustering, avoid error accumulation. The performance of the proposed algorithm is verified by the open data set and the current network user data set. The experimental results show that AFKmc2+ is compared to the AFKmc2 algorithm. Compared with tNMF algorithm, H-tNMF has better clustering effect under the same number of clustering and supports soft clustering compared with tNMF algorithm. (2) video on demand network side video on demand service card recognition, which is a problem of low quality of video transmission state data caused by acquisition conditions, proposed Based on the Caton modeling method which combines the feature construction with the black box strategy, the method overcomes the deviation in the data, and can accurately identify the video Caton, the long Caton and the multiple Caton. By analyzing the "time drift" phenomenon which leads to the low accuracy of the existing Caton recognition method, a kind of non video reconstruction is proposed, but the network side is only concerned about how to build the network side. The black box strategy of the mapping relationship between video data stream and terminal side video Caton. By analyzing the video Caton mechanism, a buffer residual data amount estimation method (Freeze Divine) based on the local difference value of the network state is proposed for mapping relationship learning. The proposed method identifies the "reconstruction + hard decision" in the existing Caton recognition scheme. Do not change to the "soft decision of estimated + data driven". By constructing the present network video card data set to verify the performance of the proposed algorithm, the experimental results show that the accuracy rate of the proposed method is 20% higher than the existing method; the correlation feature proposed by Freeze Divine is more relevant than its feature and video carton. (3) network The exception root of the side HTTP service response delay (spRTT:Service Provider Round Trip Time) is bound. This study aims at the unitary cause of the abnormality of the end to end business experience, and proposes a multi linear regression based modeling method for the divide and conquer scenario (DC-CoMo:Divide and Conquer based Context Modeling) and the greedy search tree based on the relative plucking. Construction method (ReasonTree), high efficiency calibration anomaly, accurate bound anomaly root cause. Propose DC-CoMo algorithm: use hierarchical dimension aggregation idea to reduce spRTT time series sparsity; use divide and conquer idea to model mass spRTT time series, weigh the complexity and accuracy of spRTT scene model; modeling using multiple linear regression method, comprehensive consideration of anomaly points And the comprehensive effect of the scene on spRTT. Aiming at the problem that the existing business KQI exception bound method is difficult to be fixed at the same time, the ReasonTree algorithm is proposed. The algorithm first uses the relative entropy to quantify the diversity of the anomaly area by using the relative entropy to quantify the anomaly area. The way of cable tree is used to calculate the anomaly score of the combination of different scene attributes, and the boundary multidimensional anomaly. The greedy iteration strategy is used to identify the abnormal leading causes and to filter the corresponding data, and the bound is multiple types of abnormality. ReasonTree can discover new abnormal root causes without the result of historical delimiting. It can use the spRTT data set and artificial injection of the current network HTTP service. The outlier dataset validates the performance of the proposed algorithm. The experimental results show that, for the three abnormal root causes of artificial injection, the proposed ReasonTree method has more than 95% anomaly root due to the demarcation accuracy, and the DC-CoMo algorithm uses a small number of models to delimit about 93% of the abnormal root cause of the traditional one by one modeling algorithm. Based on the developed xDR-Pro and KQI-Doctor platform, based on the user service data of the current network, the existing clustering, classification and anomaly detection algorithms are used and improved. The mobile users are well modeled in the refined customer experience management and the network side video on demand service card recognition is studied. And the SP response delay delimitation problem. The proposed method and strategy provide a new idea for the refinement of customer experience management for intelligent operation and maintenance.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN929.5

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