本文選題：用戶聚類 + 關鍵質(zhì)量指標��； 參考：《中國科學技術(shù)大學》2017年博士論文

【摘要】：傳統(tǒng)的基于關鍵性能指標(KPI:Key Performance Indicator)和用戶投訴的體驗運維方式已暴露諸多弊端,如:發(fā)現(xiàn)問題具有被動性、局限性,解決問題精細化程度不足等。對于電信運營商來說,探索面向智能化運維的精細化客戶體驗管理是當前及未來的重要研究領域。面向客戶體驗生命周期中體驗前、中、后三大環(huán)節(jié),智能化的客戶體驗管理應具備如下能力:在業(yè)務發(fā)生前進行個性化用戶偏好需求分析與預判,制定事前預防策略;在業(yè)務發(fā)生過程中快速發(fā)現(xiàn)體驗問題、定位/定界問題并動態(tài)實時調(diào)整;在業(yè)務結(jié)束后基于歷史數(shù)據(jù)學習進一步迭代優(yōu)化網(wǎng)絡,最終形成智能運維的閉環(huán)。本文的研究問題聚焦在體驗前用戶的個性化偏好行為建模、體驗中的準確監(jiān)測體驗問題和定界問題的相關策略。得益于大數(shù)據(jù)技術(shù)的快速發(fā)展,電信運營商逐步具備了海量用戶數(shù)據(jù)采集、存儲和計算的條件和能力,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,體驗管理復雜度極高;數(shù)據(jù)質(zhì)量不高,體驗信息難以準確刻畫;體驗異常成因復雜。針對上述挑戰(zhàn),本文具體研究內(nèi)容包括:(1)移動用戶的業(yè)務行為偏好建模,該研究針對大規(guī)模用戶的精細化體驗管理面臨的實現(xiàn)復雜度超高的問題,提出了基于于聚類的用戶行為偏好建模方式,平衡精細化的度和體驗管理的復雜度。針對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。針對基于雙側(cè)正交約束的非負矩陣分解(tNMF:Tri-Non-negative Matrix Factorization)的雙向聚類算法存在聚類個數(shù)事先未知、頻繁大矩陣相乘及硬聚類策略導致聚類效果差問題,提出的H-tNMF算法:借助密度聚類思想實現(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確定聚類個數(shù);定義雙向簇緊致度概念,實現(xiàn)子簇維度共享軟聚類,避免錯誤累積。利用公開數(shù)據(jù)集以及現(xiàn)網(wǎng)用戶數(shù)據(jù)集驗證所提算法性能,實驗結(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)絡側(cè)視頻點播業(yè)務卡頓識別,該研究針對采集條件制約導致的視頻傳輸狀態(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,可以準確識別出視頻卡頓、長卡頓和多次卡頓。通過分析導致現(xiàn)有卡頓識別方法準確率低的"時間漂移"現(xiàn)象,提出一種不進行視頻重建,而只關注如何構(gòu)建網(wǎng)絡側(cè)視頻數(shù)據(jù)流和終端側(cè)視頻卡頓間的映射關系的黑箱策略。通過剖析視頻卡頓機理,提出一種基于網(wǎng)絡狀態(tài)局部極差值的緩沖區(qū)剩余數(shù)據(jù)量估計方法(Freeze Divine)用于映射關系學習。所提方法將現(xiàn)有卡頓識別方案中的"重構(gòu)+硬判決"的識別思路轉(zhuǎn)變?yōu)?估計+數(shù)據(jù)驅(qū)動下的軟判決"。通過構(gòu)造現(xiàn)網(wǎng)視頻卡頓數(shù)據(jù)集驗證所提算法性能,實驗結(jié)果證明:所提方法的卡頓識別準確率比現(xiàn)有方法高20%;Freeze Divine所提去的卡頓相關特征較其他特征與視頻卡頓的相關性更強。(3)網(wǎng)絡側(cè) HTTP 業(yè)務響應延遲(spRTT:Service Provider Round Trip Time)異常根因定界,該研究針對端到端業(yè)務體驗異常成因不單一問題,提出基于多元線性回歸的分治場景建模方法(DC-CoMo:Divide and Conquer based Context Modeling)和基于相對摘的貪婪搜索樹構(gòu)建方法(ReasonTree),高效標定異常,準確定界異常根因。提出DC-CoMo算法:利用層次維度聚合思想降低spRTT時序稀疏性;利用分治思想進行海量spRTT時序建模,權(quán)衡spRTT場景模型的復雜度和準確度;建模采用多元線性回歸方法,綜合考慮異常點及場景對spRTT的綜合影響。針對現(xiàn)有業(yè)務KQI異常定界方法難以同時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:算法首先利用相對熵量化場景屬性對異常的區(qū)分度,定界小范圍異常;通過構(gòu)建搜索樹的方式計算不同場景屬性組合的異常得分,定界多維異常;利用貪婪迭代策略,識別異常主導原因并過濾對應數(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spRTT數(shù)據(jù)集及人工注入異常數(shù)據(jù)集驗證所提算法性能,實驗結(jié)果表明:對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平臺為基礎,以現(xiàn)網(wǎng)多點實測的用戶業(yè)務數(shù)據(jù)為支撐,通過對現(xiàn)有聚類、分類和異常檢測等算法的使用和改進,研究了面向智能運維的精細化客戶體驗管理中的移動用戶偏好建模、網(wǎng)絡側(cè)視頻點播業(yè)務卡頓識別以及SP響應延遲異常定界問題。所提方法和策略為面向智能運維的精細化客戶體驗管理提供新思路。
[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.

【學位授予單位】：中國科學技術(shù)大學
【學位級別】：博士
【學位授予年份】：2017
【分類號】：TN929.5

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本文編號：1814860