臺灣博碩士論文加值系統

近年來智慧生活空間技術日益受到重視。智慧生活空間中的應用服務大部分
為事件驅動(Event-driven)型式，將感測器取得的資訊轉換成事件(Event)，再依照
特定邏輯轉換為事件樣式(Pattern)，偵測出高階情境，進而提供居住者相對應的
服務。為讓此類應用程式易於開發，本論文提出一個以複雜事件分析處理技術
(Complex Event Processing, CEP)為核心的事件驅動架構，在不同的時間與空間情
境中進行分析與過濾，自動產生高階情境。為驗證此設計之可行性，本研究擴充
了開放源碼的CEP 處理核心Esper，開發了一個適用於智慧家庭的CEP 引擎。
根據實驗結果顯示，本論文提出設計在偵測較為複雜的情境事件時，不但在效能
上比傳統方式好，而且也可以大幅降低程式開發的複雜度。

Recently, the Smart Home technologies have received a lot of attentions. Based on
the idea of the event processing, sensor readings can be converted to base events. In
accordance with specific logic, they are transformed into event patterns and detect
pattern, then to acquire high-level contexts, such as human activities, and providing
appropriate services accordingly. This thesis presents a method that uses Complex
Event Processing (CEP), which performs analysis, filtering and transformation by
Event Processing Agents (EPA) to meet user-defined logic in different temporal and
spatial contexts and then generates high-level events. To verify the feasibility of the
proposed approach, a CEP engine is developed based on Esper, an open source CEP
engine. We use high-level context of smart environment compared with the multithread
development method. The result showed that the method proposed in this paper,
performance is better than used multithread for development when detect complex
event and significantly reduce the complexity of program development.

誌謝 i
摘要 ii
Abstract iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 前言 1
1.1 研究動機 1
1.2 研究問題及目標 2
1.3 研究貢獻 3
1.4 論文架構 3
第二章 背景與相關研究 4
2.1 Complex Event Processing (CEP) 4
2.2 Event Processing Agent (EPA) 5
2.3 CEP在智慧生活空間中的應用 11
第三章 複雜事件分析處理架構 12
3.1 CEP事件類型 12
3.2 CEP事件分析處理 13
3.3 CEP Context 17
3.4 CEP EPL 21
3.5 CEP Pattern 22
第四章 智慧家庭環境中情境之映射與實作 26
4.1 系統架構 27
4.2 CEP Adapter 30
4.3 智慧家庭環境中情境之映射 31
4.3.1 時間情境 Temporal Context 31
4.3.1.1 固定時間間隔Fixed Interval 31
4.3.1.2 事件觸發時間間隔 Event Interval 34
4.3.1.3 移動的固定時間間隔 Sliding Fixed Interval 38
4.3.1.4 移動的事件觸發時間間隔 Sliding Event Interval 39
4.3.2 空間情境 Spatial Context 41
4.3.2.1 固定地點 Fixed Location 41
4.3.2.2 實體距離位置 Entity Distance Location 43
4.3.2.3 事件距離位置 Event Distance Location 45
4.4 整合應用說明 47
第五章 實驗與討論 50
5.1 簡單事件效能實驗 50
5.2 Fixed Interval效能實驗 51
5.3 Sliding Fixed Interval效能實驗 52
5.4 正確性分析 55
5.5 程式複雜度分析 56
第六章 結論與未來研究 61
參考文獻 62
附錄一：相關發表著作 64

[1]C.S. Shankar, A. Ranganathan, and R.H. Champbell, “An ECA-P Policy-based Framework for Managing Ubiquitous Computing Environments,“ Proc. of the Second Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous05), pp. 33-42, 2005.
[2]H. Storf, M. Becker, M. Riedl, “Rule-based Activity Recognition Framework: Challenges, Technique, and Learning, ” Proc. of the 4th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth2009), pp. 1-7, 2009.
[3]G. Sharon, & O. Etzion, ”Event-processing network model and implementation, “ IBM Systems Journal, Vol. 47, No. 2, pp. 321-334, 2008.
[4]C.F. Liao, Y.W. Jong, and L.C. Fu, “Toward Reliable Service Management in Message-Oriented Pervasive Systems,” IEEE Transactions on Services Computing, Vol. 4, No. 3, pp. 183-195. DOI:10.1109/TSC.2010.59, 2011.
[5]D. Luckham, “The Power of Events - An Introduction to Complex Event Processing in Distributed Enterprise Systems,” Addison Wesley, 2002.
[6]G. Cugola, & A. Margara, “Processing Flows of Information: From Data Stream to Complex Event Processing,“ ACM Computing Surveys, Vol. 44, No. 3, DOI:10.1145/2187671.2187677, 2012.
[7]N. Leavitt, “Complex-Event Processing Poised for Growth,“ IEEE Computer, Vol. 42, No. 4, pp. 17-20, 2009.
[8]T. Bernhardt, & A. Vasseur, “Esper: Event Stream Processing and Correlation,” http://onjava.com/pub/a/onjava/2007/03/07/esper-event-stream-processing-and-correlation.html, accessed 2012.12.22, 2007
[9]J. C. Augusto, & C. D. Nugent, “The Use of Temporal Reasoning and Management of Complex Events in Smart Homes,” Proc. of European Conference on Artificial Intelligence (ECAI 2004), pp. 778-782, Valencia, Spain, 2004
[10]D. O. Keeffe, J. Bacon, “Reliable Complex Event Detection for Pervasive Computing,” Proc. of International Conference on Distributed Event-Based Systems (DEBS2010), pp. 73-84, 2010
[11]N. Dindar, C. Balkesen, K. Kromwijk, N. Tatbul, “Event Processing Support for Cross-Reality Environments,“ IEEE Pervasive Computing, Vol. 8, No. 3, pp. 34-41, 2009.
[12]J. Søberg, V. Goebel, T. Plagemann, “CommonSens: Personalisation of Complex Event Processing in Automated Homecare,“ Proc. of the Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2010), pp. 275-280, December 2010.
[13]G. Mühl, L. Fiege, P. Pietzuch, ”Distributed Event-based Systems,” Springer-Verlag, Berlin Heidelberg, 2006.