臺灣博碩士論文加值系統

本研究採用OLAP編碼方式，以及用於處理大數據資料的Hadoop為架構，針對心臟衰竭的病患資料，進行分維和預測病況的結點之架構整合。在編碼部分，根據病患的加入順序，結合以個人資訊維、地區維、時間維、感測維、心知識維五大維度去做分層。依照編碼的三大原則，推算出列舉之案例以兩千人為單位的醫療體系將不堪交易量的與日俱增。為解決不確定因子如:突如其來的環境因子造成的交易數據量過多，本研究提出資料蒐集應結合醫學知識為基礎的基於Hadoop的OLAP系統架構，進而降低數據量，從傳統的紀錄方式164.79 GB降低到14GB，相比約少了11.7倍。
在基於Hadoop的OLAP系統架構裡面，本研究為強調Hadoop優異的MapReduce計算與分析方式，將以個案的形式描述一個患有心律不規律波型的患者的處理流程。用於分析上應具備的四大流程包括:計數、分類、過濾處理、相關計數。此外還要建立一個造訪資料的流程，以交代初始資料的建置、修改以及運用。本研究列出偽代碼，以及Hadoop在分布式文件系統中運用的map reduce組件之間的訊息傳遞流程和相關的外加功能Hive資料倉庫工具，提供強大的類SQL查詢功能。當中會敘述到基於Hadoop的OLAP系統架構中數個模塊間的使用，以及經過MapReduce運算處理後的新表單。

In this study, we use OLAP coding method and Hadoop, which is used to process big data and construct the node framework integration of fractal dimension and condition prediction for patient data with heart failure. In the coding part, according to the joining sequence of the patients, it combines personal information dimension, regional dimension, time dimension, sensing dimension, and heart knowledge dimension, with the five dimensions for stratification. In accordance with the three principles of coding, it is projected that the amount of transactions for a medical system with listed cases of two thousand people will be unbearably increasing day by day.
In order to solve the problem of uncertain factors, such as sudden environmental factor causing excessive data, this study proposes that data collection should be combined with medical knowledge based on Hadoop OLAP system architecture, thereby to reduce the amount of data from the traditional recording method 164.79 GB to 14 GB, comparatively about 11.7 times less. This study lists pseudo-code, and Hadoop in the distributed file system used in the map reduce component between the message delivery process and related functions hive data warehouse tool to provide a powerful class SQL query function. And it describes the use of several modules in the Hadoop-based OLAP system architecture, as well as new forms that have been processed by MapReduce operations.

摘 要.................i
ABSTRACT.................ii
致謝詞...................iii
目錄 ……………………………………...iv
表目錄 ………………………………………….vi
圖目錄 …………………………………...vii
第一章 研究背景 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究貢獻 2
第二章 文獻探討 3
2.1 大數據簡介 3
2.1.1 資料量大小 3
2.1.2 資料類型 3
2.1.3 資料時效性 4
2.2 大數據的處理 6
2.2.1 大數據存儲 6
2.2.2 高性能計算 6
2.2.3 系統容錯性 7
2.3 大數據處理架構 9
2.4 醫療大數據 13
2.4.1 移動健康服務 13
2.4.2 醫療大數據的種類 13
2.4.3 個性（別）化醫療 13
2.5 疾病問題 15
2.5.1 心臟血管系統 15
2.6 心臟建模 17
2.7 感測器 18
2.8 Hadoop原理與運行機制20
2.8.1 HDFS組件 21
2.8.2 MapReduce組件 22
2.8.3 Hadoop相關的外加功能簡介24
第三章 研究方法 25
3.1 數據挖掘的功能選擇 25
3.2 數據挖掘的過程 26
3.2.1 確定系統挖掘的目標 26
3.2.2 數據選取 26
3.2.3 數據淨化 26
3.2.4 數據降維和轉換 27
3.3 聯機分析處理（OLAP）27
3.3.1 OLAP 的特點 28
3.4 Hadoop簡介 29
3.4.1 MapReduce工作機制 29
3.4.2 MapReduce作業運行流程 30
第四章 基於Hadoop的OLAP系統架構 34
4.1 確定系統挖掘的目標 34
4.2 數據選取依照Hadoop的Hbase技術集成資料36
4.3 基於OLAP的HDFS編碼39
4.3.1 按維分割 42
4.3.2 建立編碼 45
4.3.3 維層次編碼 45
4.3.4 操作I／O的計算 47
4.4 基於mapreduce設計模型49
4.4.1 計數 50
4.4.2 分類 51
4.4.3 過濾處理 55
4.4.4 相關計數分析 57
4.4.5 相關計數分析實例 58
4.4.6 相關計數分析在MapReduce運行框架中的流程65
第五章 結論 66
參考文獻 68

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