由於生醫與工業技術的不斷創新，電腦科技的持續開發，資料產生與蒐集方式急遽改變，資料規模急速成長，而資料品質易參差不齊；相關資料檢驗與分析等統計方法之需求也隨之產生，巨量資料之計算技術與統計分析方法更是目前重要的研究趨勢。視覺化(visualization)與探索式資料分析 (Exploratory Data Analysis, EDA)在巨量資料的深層分析(deep analytics)將扮演重要的角色，但也有其待解決的問題與開發的技術。目前巨量資料視覺化環境偏向以節點-連結圖(node-link diagrams)為主呈現方式的動態網絡圖(dynamic network drawing)，其基本技術為既有的 2D、3D 散佈圖(scatterplot)，其優點為較不耗記憶體、計算能力與顯示空間；但是能夠呈現的資料維度相對有限。本研究首先針對矩陣視覺化(matrix visualization)分析連續型巨量資料的兩個困難進行探討：(一) 關係矩陣計算與排序之運算能力限制。(二)在有限螢幕範圍有效顯示巨量資料的矩陣視覺化。我們使用廣義相關圖(Generalized Association Plots)結合象徵性資料分析(Symbolic Data Analysis)與Hadoop/Spark計算環境來進行矩陣視覺化及叢聚分析，藉以突破巨量資料於關係矩陣計算、排序與呈現時所面臨的困難，並提供一套可以觀看連續型巨量資料之 EDA 工具。我們以台灣全民健康保險研究資料庫之百萬抽樣檔作為實際範例，呈現巨量資料之矩陣視覺化之分析結果。當完成連續型巨量資料矩陣視覺化環境開發後，未來將進行非連續型巨量資料之矩陣視覺化方法探勘，這包含二元、類別、混合、地圖學等可能形態巨量資料之矩陣視覺化；也將面臨與連續型巨量資料矩陣視覺化環境開發不同之挑戰。

The innovation of biomedical and industrial techniques with continued development of computer technology have caused dramatic changes of data generation and collection. Data scale tends to grow exponentially while data quality becomes unreliable. Statistical methods for validation and analysis of big data with its computation techniques became important research topics nowadays. Visualization and exploratory data analysis (EDA) are going to play essential roles in deep analytics on big data analysis. Yet there are some problems to be solved and techniques to be developed. Most current big data visualization methods focus on node-link diagram based dynamic network drawing. They mainly rely on the 2D and 3D scatterplots that do not consume much computing memory, power, and display space; however, the drawback is the limitation on dimensions of variable for visualization. This works first aims to resolve the potential difficulties for applying the techniques of matrix visualization for continuous type big data: (1) computation and permutation of proximity matrices; (2) display of big data. We shall integrate the strength of GAP (generalized association plots), SDA (symbolic data analysis), with Hadoop/Spark computing facility for taking care of these problems of computation and display and for creating environment for matrix visualization of continuous type big data. Here we apply the proposed MV for big data techniques on the 2000 Longitudinal Health Insurance Database (LHID2000) of National Health Insurance Research Database (NHIRD) published by National Health Research Institutes (NHRI) in Taiwan. We will then move on and expand the environment for matrix visualization of continuous type big data to binary, categorical, cartography, and other types of big data. We expect to face even more challenging difficulties while developing related techniques.

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