隨著資訊時代的日新月異，大數據系統逐漸邁向巔峰，每天都有數以兆計的資料產生，而我們是否可以從這些制式化的數字中看出彼此之間的關聯性，甚至是整體未來的走向是一個重要的議題。我們可以透過不同的圖形來表現資料的特性尋找出其具有的規律、並分析建立模型，達到預測未來趨勢甚至是提前建立未來資料庫。
目前現有的視覺化系統多侷限在即時亦或是過去的資料形式，而較無法讓使用者了解更多未來可能出現的狀況。另外對於多數一般的使用者其操作是否足夠簡單讓其可以自行操作得到想要的視覺化圖形，是否能夠在提升視覺化的多元性的前題下讓使用者可以用更少的操作難度與耗費時間，卻可以得到更多資料的相關性，甚至是特定數值的區域分布。
本篇論文的目的在探討現有的串流大數據資料視覺化系統應用及擴展性，並提出3種具有預測性資料視覺化的方法達到更加多元化的搜尋及繪圖，並且透過Open Source進行實現，包含了軌跡預測視覺化、預測軌跡密度視覺化、IoT預測資料視覺化，希望能透過這些方法提升使用者對於資料的掌握，對未來可能發生的狀況提前作出決策從而避開風險。另外，希望透過簡化使用者對於系統的操作，讓使用者可以用較簡單的方式達到複合多元的搜尋並完成視覺化。

With the rapid development of the information age, the big data system is gradually reaching its peak, and trillions of data are generated every day. Can we find the correlation between these standardized numbers, and even the future direction. It is an important issue. We can use different graphs to express the characteristics of the data to find out the laws it has, and analyze and build models to predict future trends and even build future databases in advance. At present, the existing visualization systems are mostly limited to the real-time or past data form, and are less able to allow users to understand more possible future situations. In addition, for most common users, whether the operation is simple enough to allow them to obtain the desired visual graphics by themselves, and whether it can be easier to use and time-consuming under the premise of improving the diversity of visualization. But more data correlation, and even the regional distribution of specific values can be obtained.
The purpose of this paper is to discuss the application and scalability of existing big data stream visualization systems, and to propose three predictive data visualization methods to achieve more diversified search and drawing, including Trajectory Prediction Visualization, Predicted Trajectory Density Visualization, IoT Data Prediction Visualization. It is hoped that through these methods, users can improve their grasp of data, and make decisions in advance to avoid possible future situations. In addition, it is hoped that by simplifying the user's operation of the system, the user can achieve complex and diverse searches and complete visualization in a simpler way.

致謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vii
表目錄 ix
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的與方法 2
1.3 研究成果 2
1.4 論文架構 3
第2章 相關工作 5
2.1 物聯網(IoT) 5
2.2 串流資料(Streaming Data) 6
2.3 城市智能交通狀況監測系統 7
2.4 大數據分析可視化系統 9
2.5 相關系統工具 10
2.5.1 Kafka 10
2.5.2 MongoDB 12
2.5.3 PySpark 13
2.5.4 Apache Echarts 14
2.5.5 Apache Http Server 15
第3章 串流大數據預測式視覺化系統架構 17
3.1 研究議題與解決策略 19
3.2 串流大數據預測式視覺化系統架構 19
3.3 視覺化方法 22
3.4 視覺化方法拓展 22
第4章 串流大數據預測式視覺化方法 25
4.1 軌跡預測視覺化策略與實現 25
4.2 密度預測視覺化策略與實現 29
4.3 IoT監測資料預測視覺化策略與實現 32
第5章 系統實作效果與效能評估 35
5.1 實驗環境 35
5.2 實驗資料 36
5.3 實驗結果 37
5.3.1 軌跡預測視覺化效果實驗結果 37
5.3.2 軌跡預測視覺化準確性實驗結果 39
5.3.3 軌跡預測視覺化效能實驗結果 41
5.3.4 密度預測視覺化效果實驗結果 43
5.3.5 密度預測視覺化準確性實驗結果 45
5.3.6 密度預測視覺化效能實驗結果 46
5.3.7 IoT監測資料預測視覺化效果實驗結果 47
5.3.8 IoT監測資料預測視覺化準確性實驗結果 48
5.3.9 IoT監測資料預測視覺化效能實驗結果 50
5.3.10 架構效能實驗結果 54
5.4 實驗總結 55
第6章 結論與未來工作 57
6.1 結論 57
6.2 未來工作 57
參考文獻 59

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