本文選題：APCM + 分布式系統(tǒng)�。� 參考：《西安建筑科技大學(xué)》2014年碩士論文

【摘要】：隨著云服務(wù)應(yīng)用的越來越規(guī)范化、市場化和標(biāo)準(zhǔn)化，其他行業(yè)也隨之提供相應(yīng)的云服務(wù)。在國外醫(yī)療行業(yè)，云服務(wù)已經(jīng)進入社會化生活，提供了成熟的云醫(yī)療應(yīng)用，使得醫(yī)療資源均衡分布，用戶可以不受時間、地域的限制，通過網(wǎng)絡(luò)利用終端智能設(shè)備隨時隨地獲取醫(yī)療信息。在國內(nèi)，將云服務(wù)用于醫(yī)療領(lǐng)域剛剛起步，并沒有達到國外規(guī)范化和市場化的程度，并且，國內(nèi)的云醫(yī)療服務(wù)目前重點在于建立病歷檔案和信息交互上，對醫(yī)療數(shù)據(jù)進行數(shù)據(jù)分析的云服務(wù)并未有較大發(fā)展。因此，如何建立一種以分布式存儲為基礎(chǔ)、以數(shù)據(jù)挖掘的方法分析數(shù)據(jù)為功能的輔助醫(yī)療平臺系統(tǒng)，已成為當(dāng)前云醫(yī)療服務(wù)中的一個研究熱點。 本文的主要工作是搭建云輔助醫(yī)療平臺APCM（Assistant Platform forCloud-Medical），具體工作包括以下四個方面。 1、搭建分布式存儲系統(tǒng)和分布式計算框架，利用分布式存儲系統(tǒng)存儲大規(guī)模數(shù)據(jù)并使用分布式計算框架執(zhí)行粗粒度計算。 2、針對分布式計算的粗粒度計算所引起的效率問題，在本地節(jié)點中加入高性能計算，將節(jié)點中的計算進一步細(xì)粒度化，解決分布式計算過于粗糙的問題，，并加速了數(shù)據(jù)分析的相關(guān)計算。 3、針對聚類劃分算法K-Means聚類中心不穩(wěn)定的問題，探索了一種新的聚類算法AIK-Means，該算法利用層次聚類算法Chameleon改進K-Means算法的初始聚類中心，并且將關(guān)聯(lián)分析中的FP-Tree樹算法用于處理K-Means算法的多次聚類結(jié)果，從而找到頻繁項集為最終結(jié)果。將AIK-Means算法和高斯回歸算法基于分布式策略，使之并行執(zhí)行，提高效率。 4、針對本地節(jié)點異構(gòu)數(shù)據(jù)的訪問問題，設(shè)計并實現(xiàn)了統(tǒng)一資源訪問中間件URAM（Uniform Resource Access Middleware），分布式系統(tǒng)可以對異構(gòu)數(shù)據(jù)實現(xiàn)透明訪問。
[Abstract]:As the application of cloud services becomes more and more standardized, marketization and standardization, other industries also provide corresponding cloud services. In the foreign medical industry, cloud service has entered into social life, providing mature cloud medical applications, so that medical resources can be evenly distributed, and users can be free from time and geographical constraints. Access to medical information anytime and anywhere through the use of terminal intelligent devices through the network. At home, cloud services are just beginning to be used in the medical field, and they have not reached the level of standardization and marketization abroad. Moreover, the current focus of domestic cloud medical services is on establishing medical records and information exchange. Cloud services for data analysis of medical data have not developed significantly. Therefore, how to establish an auxiliary medical platform system based on distributed storage and analysis of data by data mining has become a research hotspot in cloud medical service. The main work of this paper is to build APCM(Assistant Platform for Cloud-Medical platform, which includes four aspects. 1. The distributed storage system and the distributed computing framework are built. The distributed storage system is used to store large-scale data and the distributed computing framework is used to perform coarse-grained computing. 2. Aiming at the efficiency problem caused by coarse-grained computing in distributed computing, high performance computing is added to the local node to further fine-grained the computation in the node, so as to solve the problem that the distributed computing is too rough. It also accelerates the calculation of data analysis. 3. Aiming at the instability of clustering center in K-Means, a new clustering algorithm, AIK-Means, is explored. The hierarchical clustering algorithm (Chameleon) is used to improve the initial clustering center of K-Means algorithm. The FP-Tree tree algorithm in association analysis is used to deal with the multiple clustering results of the K-Means algorithm, and the frequent itemsets are found as the final results. The AIK-Means algorithm and the Gao Si regression algorithm are implemented in parallel based on the distributed strategy to improve the efficiency. 4. Aiming at the problem of heterogeneous data access of local nodes, a unified resource access middleware (URAM(Uniform Resource Access Middleware) is designed and implemented. The distributed system can access heterogeneous data transparently.
【學(xué)位授予單位】：西安建筑科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP393.09;TP311.52

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