隨著人工智慧相關領域的研究，智慧型推薦系統已成功地被運用在電子商務、多媒體內容等平台，有效地幫助使用者由大量複雜資料中尋找符合其偏好與習性的資訊或產品。然而當智慧型手機的功能及操作方式日益複雜的同時，卻尚未出現一有效的方法，能依照使用者的習性，輔助其在複雜的選單上進行操作，以選擇最符合其需求的服務與功能。 本研究期望基於相關文獻理論的基礎，結合推薦系統及情境感知系統的架構，設計改良一預測使用者操作手機之意圖的演算法，使系統能夠經由獲取使用者操作手機的模式與所處情境的關係，分析其使用的習性，以藉此在特定情境下，透過本演算法的運算，預測使用者意圖進行的動作，以減少其選擇手機功能的複雜度。期望經由演算法的設計與系統實證後，能夠發展一有效的演算法策略以符合預測智慧型手機使用者操作意圖之需要，並以實驗證明其可行性及效用。

With the development of artificial intelligence , the application of recommender systems has been extended to fields such as e-commerce shopping cart analysis or video recommendation system. These systems provide user a recommended resource set based on their habits or behavior patterns to help users saving searching cost. However, these techniques have not been successfully adopted to help users search functions on smart-phones more efficiency. This research is designated to build the context-aware system, which can generate the list of operations predicting which function user might use under certain contexts through continuously learning users operation patterns and related device perceived scenario. The system utilize event-condition-action patterns to describe user frequent behaviors, and the research will focus on developing innovative Action-Condition-Fit algorithm to figure the similarity between action pattern sets and real-time scenario. Proposed system and algorithm will then be built on Google App Engine and Android device to empirically validate its performance through field test.

論文審定書 1
誌謝 2
摘要 3
Abstract 4
目錄 5
圖目錄 7
表目錄 9

第一章 緒論
1.1. 研究背景 1
1.2. 研究動機與目的 3

第二章 問題描述 5

第三章 文獻探討
3.1 情境感知運算 7
3.2 Event-Condition-Action模式 10
3.3 ECA規則與規則引擎 13
3.4 規則學習 16
3.5 Apriori演算法 19
3.6 Fuzzy c-means Clustering演算法 23
3.7 相關研究整理 25

第四章 研究方法
4.1 系統架構 26
4.2 系統之週邊模組 27
4.3 系統之演算法模組 28
4.4 演算法流程及策略 30
4.5 系統演算法策略範例說明 41
4.6 系統實作平台 45
4.7 系統運作情境實例 46

第五章 實驗與結果分析
5.1 實驗設計 47
5.2 實驗結果評估方法 54
5.3 實驗資料與參數設定 56
5.4 實驗資料與結果分析 59

第六章 結論與未來展望
6.1 研究結論與貢獻 68
6.2 研究限制 68
6.3 未來研究方向 69

參考文獻 70

1. B. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” in
Mobile Computing Systems and Applications, 1994. WMCSA 1994. First Workshop
on, 1994, 85–90.
2. A.K. Dey, “Understanding and using context,” Personal and ubiquitous computing 5,
no. 1 (2001): 4–7.
3. (Pnalized Content Recommendation and User Satisfaction: Theoretical Synthesis and
Empirical Findings Ting-Peng Liang)
4. M. Weiser. The computer for the 21st century. Scientific American, 265(3):66–75,
September 1991
5. R. Want, B. N. Schilit, N. I. Adams, R. Gold, K. Petersen, D. Goldberg, J. R. Ellis,
and M. Weiser. An overview of the parctab ubiquitous computing environment. IEEE
Personal Communications, 2(6):28–43, 1995.
6. A. Harter, A. Hopper, P. Steggles, A. Ward, and P. Webster. The anatomy of a
context-aware application. Wireless Networks, 8(2/3):187–197, 2002
7. T.P. Moran及P. Dourish, 「Introduction to this special issue on context-aware
computing」, Human-Computer Interaction 16, 期. 2-4 (2001): 87–95.
8. B.N. Schilit and M.M. Theimer, “Disseminating active map information to mobile
hosts,” Network, IEEE 8, no. 5 (1994): 22–32.
9. “A Survey of Context-Aware Mobile Computing Research.pdf”, n.d.“
10. B.N. Schilit and M.M. Theimer, “Disseminating active map information to mobile
hosts,” Network, IEEE 8, no. 5 (1994): 22–32.
11. B. Schilit, N. Adams, and R. Want, “Context-aware computing applications,” in
Mobile Computing Systems and Applications, 1994. WMCSA 1994. First Workshop
on, 1994, 85–90.
12. A.K. Dey, “Understanding and using context,” Personal and ubiquitous computing 5,
no. 1 (2001): 4–7.
13. "Why is Siri important". Brian Roemmele. 2011-10-10. Retrieved 2011-10-29.
14. “Context Awareness to Radically Change How We Interact with Technology.” Justin
Rattner, Intel CTO, 2010-09-15
15. “SENS – Socially Enabled Services for Mobile Devices.” Micheal Johnson, 2010-09-
27
Department of Information Management, National Sun Yat-sen University 70
16. “Gartner Predicts 2012 | Information Technology Predictions”, Gartner Inc. 2011-
Nov
17. “Key Issues for Context-Aware Computing” Gartner Inc. 2011
18. D. Zhang and M. Mokhtari, “A New Architecture for Smart Homes Based on ADB
and Temporal Reasoning,” in Toward a human-friendly assistive environment:
ICOST’2004, 2nd International Conference on Smart Homes and Health Telematics,
vol. 14, 2004, 106.
19. Y. Qiao et al., “Developing event-condition-action rules in real-time active database,”
in Proceedings of the 2007 ACM symposium on Applied computing, 2007, 511–516.
20. M.J. van Sinderen et al., “Supporting context-aware mobile applications: an
infrastructure approach,” Communications Magazine, IEEE 44, no. 9 (2006): 96–104.
21. C.L. Forgy, “Rete: A fast algorithm for the many pattern/many object pattern match
problem,” Artificial intelligence 19, no. 1 (1982): 17–37.
22. Jess, the Rule Engine for the Java Platform
23. CLIPS: A Tool for Building Expert Systems
24. Jena Semantic Web Framework
25. JLisa - A Rule Engine for Java
26. W. Beer et al., “Modeling context-aware behavior by interpreted ECA rules,” Euro-
Par 2003 Parallel Processing (2003): 1064–1073.
27. T. Beer et al., “Exploiting ECA rules for defining and processing context-aware push
messages,” in Proceedings of the 2007 international conference on Advances in rule
interchange and applications, 2007, 199–206.
28. RIPPER(William W. Cohen, Fast Effective Rule Induction.)
29. CN2(Clark and Niblett 1989; Clark and Boswell 1991)
30. APRIORI-C (Jovanoski and Lavracˇ 2001)
31. Methods for Rule Conflict Resolution Lindgren(2004)
32. Rule Votes RuleQuest Research. See 5: an informal tutorial, 2003.
33. T. Lindgren and H. Bostr‥om. Classification with intersecting rules. In Proceedings of
the 13th International Conference on Algorithmic Learning Theory (ALT’02), pages
395–402. Springer-Verlag, 2002.
34. C.M. Antunes and A.L. Oliveira, “Temporal data mining: An overview,” in KDD
Workshop on Temporal Data Mining, 2001, 1–13.
Department of Information Management, National Sun Yat-sen University 71
35. Gavrilov, M., Anguelov, D., Indyk, P, Motwani, R.. Mining The Stock Market:
Which Measure Is Best?. KDD (2000) 487-496
36. Coiera, E.: The Role of Knowledge Based Systems in Clinical Practice. In
Barahona, P., Christensen, J.: Knowledge and Decisions in Health Telematics –
The Next Decada. IOS Press Amsterdam (1994) 199-203
37. Das, G., Mannila, H., Smyth, P.: Rule Discovery from Time Series. KDD (1998) 16-
22
38. Keogh, E., Pazzani, M.: An enhanced representation of time series which allows
fast and accurate classification, clustering and relevance feedback. Knowledge
Discovery in Databases and Data Mining (1998) 239-241
39. Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast Subsequence Matching in
Time-Series Databases. ACM SIGMOD Int. Conf. on Management of Data (1994)
419-429
40. Han, J., Pei, J., Mortazavi-Asl, B., Chen, Q., Dayal, U., Hsu, M.: FreeSpan:
Frequent pattern-projected sequential pattern mining. ACM SIGKDD (2000) 355-359
41. Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. ICDE (1998)
412-421
42. Oates, T.: Identifying Distinctive Subsequences in Multivariate Time Series by
Clustering. KDD (1999) 322-326
43. O. Kwon, S. Choi, and G. Park, “NAMA: a context-aware multi-agent based web
service approach to proactive need identification for personalized reminder systems,”
Expert Systems with Applications 29, no. 1 (2005): 17–32.
44. H. Si, Y. Kawahara, H. Morikawa, T. Aoyama, "A stochastic Approach for Creating
Context-Aware Services based on Context Histories in Smart home," ECHISE2005,
Pervasive 2005, P37-41
45. T. Ma, Y. D. Kim, Q. Ma, M. Tang, W. Zhou, "Context-Aware Implementation based
on CBR from smart home," ECHISE2005, Pervasive 2005, P37-41
46. J. Choi, D shin, D shin – Consumer Electronics “Research and Implementation of the
context-Aware Middleware for Controlling Home Appliance,” Consumer Electronics,
IEEE Transactions, Volume: 51, Issue: 1, P 301-306
47. H. Chen, F. Perich, D. Chakraborty, T. Finin, A. Joshi, "Intelligent Agents Meet
Semantic Web in a Smart Meeting Room," Autonomous Agents and Multiagent
System, 2004. AAMAS 2004.
Department of Information Management, National Sun Yat-sen University 72
48. Krause, A. Smailagic, D. P. Siewiorek, "Context-Aware Mobile Computing Learning
Context-Dependent Personal Preferences from a Wearable Sensor Array," IEEE
Transcations on Mobile Computing 5:22, 113-127, 2006.
49. A. Roy, S. K. Das and K. Basu, “A Predictive Framework for Location-Aware
Resource Management in smart homes” IEEE Transaction on Mobile Computing, Vol.
6, No. 11, November, 2007.
50. GE Yap, AH Tan, HH Pang, "Discovering Causal Dependencies in Mobile
Context-Aware Recommenders," Mobile Data Management, 2006 MDM 2006. 7th
International Conference on Publication Date:10-12 May 2006
51. H. Chen, T. Finin, A. Joshi, “An Ontology for Context-Aware Pervasive Computing
Environments,” The Knowledge Engineering Review (2003), 18: 197-207 2004
52. B. Ahmed, Y. K. Lee, S. Lee, Y. Zhung, “Scenario Based Fault Detection in Context-
Aware Ubiquitous Systems using Bayesian Networks.” Computational Intelligence for
Modeling, Control and Automation, 2005 and International Conference, Volume: 1, P:
414-420, Nov 2005.
53. Krause, A. Smailagic, D. P. Siewiorek, "Context-Aware Mobile Computing Learning
Context-Dependent Personal Preferences from a Wearable Sensor Array," IEEE
Transcations on Mobile Computing 5:22, 113-127, 2006.
54. T. Ma, Y. D. Kim, Q. Ma, M. Tang, W. Zhou, "Context-Aware Implementation based
on CBR from smart home," ECHISE2005, Pervasive 2005, P37-41
55. Bezdek, J., "FCM: The fuzzy c-means clustering algorithm", Computers &
Geosciences, vol. 10, issue 2-3, pp. 191-203.
56. H. Chen, F. Perich, D. Chakraborty, T. Finin, A. Joshi, "Intelligent Agents Meet
Semantic Web in a Smart Meeting Room," Autonomous Agents and Multiagent
System, 2004. AAMAS 2004.
57. E. Trauwaert, "On the meaning of Dunn's partition coefficient for fuzzy clusters",
Fuzzy Sets and Systems, Volume 25, Issue 2, February 1988, Pages 217–242.
58. James C. Bezdek, "Cluster Validity with Fuzzy Sets", Journal of Cybernetics, Volume
3, Issue 3, 1973.
59. Rakesh Agrawal, Ramakrishnan Srikant, "Fast Algorithms for Mining Association
Rules in Large Databases", VLDB '94 Proceedings of the 20th International
Conference on Very Large Data Bases, Pages 487 - 499.