情節樣式探勘是資料探勘技術中一種用以獲取高價值資訊的重要技術，可以為人們解決現實生活中各種不同領域的問題，例如製造分析、股票市場、天氣預測、醫療健康、網路資安等各領域。儘管情節模式探勘技術已存在多年，然而隨著大數據時代來臨，物聯網設備所收集的連續性序列資料的分析需求大幅成長，如何正確且高效地應用情節模式探勘技術來分析複雜事件序列資料，且必須要具備延展性以因應資料的快速成長，這對於解決各領域的應用問題變得越來越重要。但根據我們的探討，發現目前仍很少有研究專注發展一個具有延展性的架構，可將情節模式探勘技術應用於從連續性複雜事件序列資料，以協助各領域專家從中取得有用資訊。上述情況促使我們想研究如何發展出一個雛形架構，可以為各領域專家提供一種正確、高效且具有延展性的方式，可以應用情節樣式探勘技術來完成資料探勘工作。
在本論文中，我們提出了一個基於複雜事件情節探勘技術的新架構，主要貢獻在於解決以下過去方法所面臨的幾個問題：(1) 傳統的情節探勘技術多是應用在靜態資料庫去探勘高頻率情節樣式，並無法適用於連續性資料，特別是針對物聯網設備所收集的串流資料做分析，而本架構則是針對連續性串流資料的探勘所設計；(2)當有新資料產生時，若要重新取得正確的分析結果，通常必須重新執行完整的探勘過程，這是一個相當費時的過程，本架構採用Lambda架構設計，拆分為批量情節挖掘、增量情節挖掘和模式合併，以兼顧效率和準確性；(3)當資料持續增長需要更大的分析運算能力時，我們所提架構採用Apache Spark 跟 Apache Spark Streaming作為程式開發框架，具有可因應需要快速擴展運算能量之延展性。
在本論文中，我們研究跟分析這個過往少有人觸及但卻有廣大實務應用價值的主題，從理論跟實務面去設計跟開發具備上述優點的新架構，並利用不同領域的公開資料及我們收集的真實資料來評估我們架構的實用性。實驗結果顯示，我們所提的架構兼顧效率與準確性，且具有延展性，適合用於各種不同領域應用的複雜事件情節探勘分析架構。

How to get important and valued information for people to solve problems around us, such as the analysis of traffic data, healthcare improvement, weather forecast, cyber security is a booming requirement, and episode pattern mining is a very useful technique for the above purpose. Along with the rapid development of IoT (Internet of Things), there comes the ’Big Data’ era with the fast growth of digital data and the requirements rise for gaining useful knowledge by analyzing the rich data collected by those devices. How to effectively and efficiently analyze complex event data using episode pattern mining technique is becoming more and more important for solving problems in many domains. According to our study, there are very few studies focus on developing a framework based on episode pattern mining technique of complex event sequences with scalability and can be fit for applications with most domains. The situations mentioned above motivate us to focus on developing a novel framework that people use for conducting such data mining jobs effectively and efficiently.
In this dissertation, we propose a novel and scalable architecture for complex event episode mining. The main contributions are described as the following: (1) Most of the existing methods focused on mining episode patterns in static data, and they cannot fulfill the requirement of analyzing the streaming data, especially those data collected via IoT devices. The architecture we proposed is particularly designed for complex event sequence of data streams; (2) When data increases, the process of mining will be re-activated once and once to get most updated patterns, this is a high-cost process. We adopt the lambda architecture, including delta episode mining, batch episode mining, and pattern merging, and this architecture takes both efficiency and accuracy into account; (3) Moreover, to enhance scalability, we chose Apache Spark and Apache Spark Streaming as the system development framework. When more computing power is needed, it will be easy to for users scale up the system by adding more computing units.
In this dissertation, we made comprehensive survey and analysis on this topic that was less explored in the past but has great practical value in real-world applications. We design and develop an architecture with the above advantages from the theoretical and practical aspects and use datasets of different application domains, including wide-used public datasets and some real datasets we collected, to evaluate the performance of the architecture we proposed. Experimental results show the architecture outperforms other existing methods in accuracy and efficiency. As to the huge complex event episode mining jobs, the experimental result proves that our framework has a very good scalability as well. It is a scalable analytical architecture for complex event episode mining with various domains applications.

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