隨著越來越多可攜式行動裝置與數位電子產品的發展，普及運算被視為下一代重要的電腦運算技術，其目的是讓使用者可以在任何時間、任何地點、透過任何裝置來存取運算服務。因此，如何發展一個情境感知服務，成為普及運算中重要的課題。在本論文中，我們提出以代理人為基準的模組化情境感知服務中介軟體層來降低程式開發者的開發難度。除此之外，為了達到使用者的需求，情境感知服務必須透過感測環境狀態來提供使用者適當的服務內容。如果情境感知服務使用了不可靠的感測資訊，將導致不可預期的服務結果。然而，感測裝置所提供的資訊往往存在不確定性，為了解決這個問題，我們發展可靠的情境資訊模型，用以度量感測資訊的可靠性，以確保情境感知服務可以提供使用者正確的服務內容。
為了提供情境感知服務的適應性，我們提出一個新的序列比對方法，用以辨識使用者的活動內容，此方法透過記錄使用者日常生活中的活動資料，萃取出具代表性的活動特徵，藉由感測資訊與活動特徵的比對來辨識使用者的活動內容。萃取活動特徵的過程中，我們利用回溯機制找出不固定長度的使用者活動特徵，藉以避免與目前大部分的研究相同，必須利用主觀看法去定義活動中所需的每一個動作。另一方面，由於一個活動可能包含許多活動特徵，因此我們發展一個機率模型來表示特徵與活動間的相關性。此外，我們也提供動態規劃演算法來做活動與特徵間的匹配，藉以辨識使用者的即時活動內容。
最後，為了讓情境感知服務在普及計算環境中得以順利進行，透過挖掘使用者對複合性服務需求的歷史資料，找出使用者執行複合性服務的機率模型，藉以提高系統效率並減少使用者的等待時間。最後，系統利用積極主動的資源分配方法與複合性服務間的關聯規則，去預測下一個即將執行的複合性服務，並提前做資源配置以期望達到最大的複合性服務執行數量和提升系統的使用效能。
關鍵字：普及運算、情境感知、行為辨識、服務自動化

Because the popularity of mobile devices is increasing rapidly, ubiquitous computing technology and service automation techniques have become hot topics of study. Therefore, the development of a context-aware application is an important issue related to ubiquitous computing. However, it is difficult to design a context-aware application without modular middleware. Therefore, in the dissertation, we propose agent-based context-aware middleware to support the rapid development of context-aware applications. Moreover, if a context-aware application wants to achieve users’ requirements smoothly, it must understand the environment status according to reliable context information. Unfortunately, the collection of context information from sensors may be uncertain. This phenomenon may lead to an undesirable result. In order to solve this problem, we propose a reliable context model to evaluate the reliability of context information for satisfying users’ requirements.
For realizing adaptive services based on context awareness, a novel sequence alignment approach is proposed to extract the representative activity patterns from training data and recognize human activities according to the extracted patterns. This approach features a new trace-back mechanism, automatic mining of activity patterns without a fixed length, probabilistic correlations between patterns and activities, and dynamic-programming-based pattern matching. The new trace-back mechanism is invoked for pattern mining in order to successfully find all representative activity patterns. Moreover, considering that a single activity may contain multiple activity patterns and a single activity pattern may contribute multiple activities, we have developed a probabilistic model to represent the correlations between patterns and activities. The feature of the automatic mining of activity patterns without a fixed length avoids the problem of manually defining actions or stages for activities required by most existing approaches. During pattern matching, we apply the dynamic programming scheme instead of segmenting the data into subsequences with a fixed time period (e.g., time slot or sliding window). Hence, user activities can be continuously recognized immediately after a change in an incoming human-object interaction sequence. Moreover, the proposed scheme favors the recognition of concurrent activities with parallel matching.
A combination of service composition and resource allocation technologies is necessary to deploy a ubiquitous environment based on service-oriented architecture (SOA). To optimize resource utilization, the development of a proactive resource allocation approach by predicting users’ needs from historical information is becoming popular. In this dissertation, we also propose a novel service prediction method, called statistical proactive resource allocation method (SPRAM), to enhance the resource utilization efficiency of SOA. SPRAM invokes a mining algorithm to generate a statistical model for proactive resource allocation and aims to maximize the number of successfully executed composite services and minimize users’ average waiting time. For the simulation, the Markov model is adopted to generate sequential composite service patterns (CSPs) for representing the users’ usage of composite services for a certain period of time. The simulation results show that SPRAM has better performance than other scheduling algorithms.

Key Words：Context-Aware Middleware, Context Model, Activity Recognition, Proactive Resource Allocation Scheme

[AGA05] V. Agarwal, K. Dasgupta, N. Karnik, A. Kumar, A. Kundu, S. Mittal, and B. Srivastava, “A Service Creation Environment Based on End to End Composition of Web Service,” In Proceedings of the 14th International Conference on World Wide Web, Session: Web Services, ACM Press, Chiba, Japan, pp. 128-137, 2005.

[AGR95] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” In Proceedings of the 11th Int’l Conference on Data Engineering, pp. 3-14, 1995.

[BAH00] P. Bahl and V. N. Padmanabhan, “RADAR: An In-Building RF-Based User Location and Tracking System,” In Proceedings of 19th Annual Joint Conference of the IEEE Computer and Communications Societies, pp. 775-784, Tel Aviv, Israel, 2000.

[BAL07] M. Baldauf, S. Dustdar, and F. Rosenberg, “A Survey on Context-Aware System,” Technical University of Vienna Information System Institute Distributed System Group, 2007.

[BAS03] P. Basu, “A Task Based Approach for Modeling Distributed Applications on Mobile Ad Hoc Networks,” Ph.D. Thesis, Boston University, Boston, MA, May 2003.

[BEN02] B. Benatallah, M. Dumas, Q. Z. Sheng, and A. H. H. Ngu, “Declarative Composition and Peer-to-Peer Provisioning of Dynamic Web Services,” In Proceedings of the 18th International Conference on Data Engineering (ICDE’02), IEEE Computer Society, 2002.

[BER05] D. Berardi, D. Calvanese, G. De Giacomo, R. Hull, and M. Mecella, “Automatic Composition of Transition-Based Semantic Web Services with Messaging,” In Proceedings of the 31st International Conference on Very Large Data Bases (VLDB), pp. 613-624, 2005.

[CAR04] J. J. Carroll, I. Dickinson, C. Dollin, D. Reynolds, A. Seaborne, and K. Wilkinson. “Jena: Implementing the Semantic Web Recommendations,” In Proceedings of the 13th International World Wide Web Conference on Alternate Track Papers and Posters, May 19-21, 2004.

[COS00] R. S. Cost, Y. Chen, T. Finin, Y. Labrou, and Y. Peng, “Using Colored Petri Nets for Conversation Modeling,” Lecture Notes in Computer Science, vol. 1916, Issues in Agent Communication, Springer-Verlag, pp. 178-192, 2000.

[CHI05] Y. L. Ch'i and C. W. Li, “A Validating Approach for Evaluating Reliability of Web Services Compositions,” Journal of Information, Technology and Society, vol. 5, no. 2, 2005.

[CHE93] A. Cheng, J. Esparza, and J. Palsberg, “Complexity Results for 1-Safe Nets,” Foundations of Software Technology and Theoretical Computer Science, vol. 761 of Lecture Notes in Computer Science, pp. 326-337, Springer-Verlag, Berlin, 1993.

[CHE04] H. Chen, “An Intelligent Broker Architecture for Pervasive Context-Aware Systems,” PhD Thesis, University of Maryland, Baltimore County, 2004.

[CER03] H. Cervantes and R. S. Hall. “Automating Service Dependency Management in a Service-Oriented Component Model,” ICSE CBSE6 Workshop, 2003.

[DEY00] A. K. Dey, “Providing Architectural Support for Building Context-Aware Applications,” PhD Thesis, Georgia Institute of Technology, 2000.

[DOB02] P. Dobrev, D. Famolari, C. Kurzke, and B. A. Miller, “Device and Service Discovery in Home Networks with OSGi,” IEEE Communications Magazine, vol. 40, no. 40, pp. 86-92, Aug. 2002.

[DIG] Digital Living Network Alliance (DLNA), Available: http://www.dlna.org/home.

[EIK02] M. EI-Kadi, S. Olauri, and Abel-Wahab, “A Rate-Based Borrowing Scheme for QoS Provisioning in Multimedia Wireless Networks,” IEEE Transaction on Parallel and Distributed System, vol. 13, no. 2, pp. 156-166, 2002.

[FAH04] P. Fahy, and S. Clarke, “CASS – A Middleware for Mobile Context-Aware Applications,” In Workshop on Context Awareness, MobiSys 2004.

[FUN05] C. Funk, C. Kuhmünch, and C. Niedermeier, “A Model of Pervasive Service for Service Composition,” Lecture Notes in Computer Science, vol. 3762, pp. 215-224, 2005.

[GU04] T. Gu, X. H. Wang, H. K. Pung, and D. Q. Zhang, “An Ontology-Based Context Model in Intelligent Environment,” In Proceedings of Communication Networks and Distributed Systems Modeling and Simulation Conference, pp. 270-275, San Diego, California, USA, January 2004.

[GU05] T. Gu, H. K. Pung, and D. Q. Zhang. “A Service-Oriented Middleware for Building Context-Aware Services,” Journal of Network and Computer Applications, vo1. 28, Issue 1, pp. 1-18, Jan. 2005.

[GAR79] M. Gary and D. Johnson, “Computers and Intractability: A Guide to the Theory of NP-Completeness,” Freeman, 1979.

[HEN01] K. Henricksen, J. Indulska, and A. Rakotonirainy, “Infrastructure for Pervasive Computing: Challenges,” Workshop on Pervasive Computing Informatik 2001, Vienna, September 25-28, 2001.

[HAR02] A. Harter, A. Hopper, P. Steggles, A. Ward, and P. Webster, “The Anatomy of a Context-Aware Application,” Wireless Networks, Springer, Netherlands, pp. 187-197, 2002.

[HEL02] A. Held, S. Buchholz, and A. Schill, “Modeling of Context Information for Pervasive Computing Applications,” In Proceedings of the 6th World Multiconference on Systemics, Cybernetics and Informatics (SCI2002), Orlando, Florida, July 14-18, 2002.

[HOR02] I. Horrocks, and U. K. Manchester, “DAML+OIL: a Reason-able Web Ontology Language,” In Proceedings of the 8th International Conference on Extending Database Technology (EDBT), Prague, March 2002.

[HOF02] T. Hofer, W. Schwinger, M. Pichler, G. Leonhartsberger, and J. Altmann. “Context-Awareness on Mobile Devices – The Hydrogen Approach,” In Proceedings of the 36th Annual Hawaii International Conference on System Sciences, pp. 292-302, 2002.

[HUA06] P. C. Huang, K. R. Lee, W. T. Su, Y. H. Kuo, M. F. Horng, C. C. Lin, and Y. C. Chen, “Control Component Development of Information Appliances on Networks,” Journal of Information Science and Engineering, vol. 22, no. 4, pp. 771-784, 2006.

[HAN00-1] J. Han, J. Pei, and B. Mortazavi-Asl, “Freespan: Frequent Pattern-Projected Sequential Pattern Mining,” In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 355-359, 2000.

[HAN00-2] J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns Without Candidate Generation,” In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000.

[HEN04] K. Henricksen and J. Indulska, “Modelling and Using Imperfect Context Information,” In Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 33, March 14-17, 2004.

[HIG01] J. Hightower and G. Borriello, “Location Sensing Techniques,” IEEE Computer, vo1. 34, Issue 8, pp. 57-66, 2001.

[HUE04] M. C. Huebscher and J. A. McCann, “Adaptive Middleware for Context-Aware Applications in Smart-Homes,” in Proceedings of the 2nd Workshop on Middleware for Pervasive and Ad-Hoc Computing, pp. 111-116, Toronto, Ontario, Canada, October 18-22, 2004.

[INT] Proactive Computing, Intel Research Available: ftp://download.intel.com/research/prohealth/proactivepdf.pdf.

[JAD] Jade - Java Agent DEvelopment Framework, Available: http://jade.tilab.com/.

[JUD03] G. Judd and P. Steenkiste, “Providing Contextual Information to Pervasive Computing Applications,” In Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, pp. 133, March 23-26, 2003.

[Kuo04] Y. H. Kuo and M. F. Horng, “A New Era of Digital Living,” Chinese Institute of Electrical Engineering Magazine,” pp. 16-21, 2004.

[KOR03] P. Korpipää, J. Mäntyjärvi, J. Kela, H. Keränen, E. J. Malm, “Managing Context Information in Mobile Devices,” IEEE Pervasive Computing, pp. 42–51, 2003.

[KAR02] J. Karaoguz, “High-Rate Wireless Personal Area Networks,” IEEE Communications Magazine, vo1. 39, issue 12, pp. 96-102, Dec. 2001.

[KRA05] N. Krahnstoever, J. Rittscher, P. Tu, K. Chean, and T. Tomlinson, “Activity Recognition using Visual Tracking and RFID,” In Proceeding of the 7th IEEE Workshops on Application of Computer Vision, pp. 494-500, Washington DC, USA, 2005.

[KAL07] S. Kalasapur, M. Kumar, and B. A. Shirazi, “Dynamic Service Composition in Pervasive Computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 7, pp. 907-918, 2007.

[KAP07] G. Kapitsaki, G. Kateros, and D. A. Foukarakis, “Service Composition: State of the Art and Future Challenges,” Mobile and Wireless Communications Summit 16th IST, 2007.

[LEE06] S. Y. Lee, J. Y. Lee, and B. I. Lee, “Service Composition Techniques using Data Mining for Ubiquitous Computing Environments,” International Journal of Computer Science and Network Security, vol. 6, no. 9B, 2006.

[LEI02] H. Lei, D. M. Sow, J. S. Davis, II, G. Banavar, and M. R. Ebling, “The Design and Applications of a Context Service,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 6, no. 4, pp. 45-55, Oct. 2002.

[LIN06] C. Y. Lin and J. Y. Hsu, “IPARS: Intelligent Portable Activity Recognition System via Everyday Objects, Human Movements, and Activity Duration,” In Proceeding of AAAI Workshop on Modeling Others from Observations, pp. 44-52, Boston, USA, 2006.

[MCG04] D. L. McGuinness and F. V. Harmelen, “OWL Web Ontology Language Overview,” W3C Recommendation, 2004.

[MAA05] Z. Maamar, S. K. Mostefaoui, and H. Yahyaoui, “Toward an Agent-Based and Context-Oriented Approach for Web Services Composition,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 5, pp. 686-698, 2005.

[MAN04] S. S. Manapure, H. Darabi, V. Patel, and P. Banerjee, “A Comparative Study of Radio Frequency-Based Indoor Location Sensing Systems,” In Proceedings of IEEE International Conference on Networking, Sensing and Control, pp. 1265-1270, Taipei, Taiwan, 2004.

[MIN06] A. Mingkhwan, P. Fergus, and O. Abuelma'atti, “Dynamic Service Composition in Home Appliance Networks,” Multimedia Tools and Applications, vol. 29, no. 3, pp. 257-284, 2006.

[NAR02] S. Narayanan and S. A. Mcllraith, “Simulation, Verification and Automated Composition of Web Services,” In Proceedings of the 11th International Conference on World Wide Web, Session: Semantic Web Services (WWW’02), ACM Press, pp. 77-88, Honolulu, Hawaii, USA, 2002.

[NI03] M. Ni, Y. Liu, Y. C. Lau, and A. P. Patil, “LANDMARC: Indoor Location Sensing using Active RFID,” In Proceeding of the 1st IEEE International Conference on Pervasive Computing and Communications, pp. 407-415, Texas, USA, 2003.

[OMA06] M. Omar, A. Baharum, and Y.A. Hasan “A Job-Shop Scheduling Problem (JSSP) using Genetic Algorithm (GA),” In Proceedings of the 2nd IMT-GT Regional Conference, 2006.

[OWL04] Semantic Markup for Web Services, Available: http://www.w3.org/Submission/OWL-S/.

[PHI04] M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz, and D. Hähnel, “Inferring Activities from Interactions with Objects,” IEEE Pervasive Computing, vo1. 3, Issue 4, pp. 50-57, 2004.

[PHI05] M. Philipose, J. R. Smith, B. Jiang, A. Mamishev, S. Roy, and K. S. Rajan “Battery-Free Wireless Identification and Sensing,” IEEE Pervasive Computing, vol. 4, pp. 37-45, 2005.

[PAT05] D. J. Patterson, D. Fox, H. Kautz, and M. Philipose, “Fine-Grained Activity Recognition by Aggregating Abstract Object Usage,” In Proceedings of the 9th IEEE International Symposium on Wearable Computers, pp. 44-51, Osaka, Japan, 2005.

[PER04] M. Perkowitz, M. Philipose, K. Fishkin, and D. J. Patterson, “Mining Models of Human Activities from the Web,” pp. 573-582, New York, USA, 2004.

[PIL04] M. Philipose, K. P. Fishkin, M. Perkowitz, D. J. Patterson, D. Fox, H. Kautz and D. Hähnel, “Inferring Activities from Interactions with Objects,” IEEE Pervasive Computing, vol. 3, issue 4, pp. 50-57, 2004.

[PAG93] I. Page, T. Jacob, and E. Chern, “Fast Algorithms for Distributed Resource Allocation,” IEEE Transactions on Parallel and Distributed Systems, vol. 4, no. 2, pp. 188-197, 1993.

[PEI01] J. Pei, J. Han, and H. Pinto, “Prefixspan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” International Conference on Data Engineering, 2001.

[POR] Proactive Computing, Intel Research Available: ftp://download.intel.com/research/prohealth/proactivepdf.pdf.

[QIU06] Y. Qiu and Y. Lan, “Efficient Improvement of FT-Tree Based Frequent Itemsets Mining Algorithms,” In Proceedings of the First International Conference on Innovative Computing, vol. 3, 2006.

[QUI04] A. Quigley, B. Ward, C. Ottrey, D. Cutting, and R. Kummerfeld, “BlueStar, A Privacy Centric Location Aware System,” In Proceedings of Position Location and Navigation Symposium, pp. 684-689, Monterey, CA, USA, 2004.

[RAV04] B. Ravindran, and P. Li, “DPR, LPR: Proactive Resource Allocation Algorithms for Asynchronous Real-Time Distributed Systems,” IEEE Transactions on Computers, vol. 53, no. 2, pp. 201-216, 2004.

[RED07] D. Redondo, R. P. Vilas, A. F. Cabrer, M. R. Pazos Arias, and J. J. Marta Rey Lopez, “Enhancing Residential Gateways: OSGi Service Composition,” IEEE Transactions on Consumer Electronics, vol. 53, no. 1, pp. 87-95, 2007.

[ROM02] M. Román, C. Hess, R. Cerqueira, A. Ranganathan, R. H. Campbell, and K. Nahrstedt, “Gaia: A Middleware Platform for Active Spaces,” ACM SIGMOBILE Mobile Computing and Communications Review, vol. 6, issue 4, pp. 65-67. Oct. 2002.

[RED08] R. P. D. Redondo, A. F. Vilas, M. R. Cabrer, J. J. P. Arias, J. G. Duque, and A. G. Solla, “Enhancing Residential Gateways: A Semantic OSGi Platform,” IEEE Intelligent Systems, vol. 23, no. 1, pp. 32-40, Jan.-Feb. 2008.

[SIL01] A. Silberschatz, P. B. Galvin, G. Gagne and A. Silberschatz, “Operating System Concepts 6th Edition (Hardcover),” Wiley, 2001.

[SRI03] B. Srivastava and J. Koehler, “Web Service Composition – Current Solutions and Open Problems,” In ICAPS 2003 Workshop on Planning for Web Services, 2003.

[SKO04] D. Skogan, R. Gronmo, and I. Solheim, “Web Services Composition in UML,” In Proceedings of the 8th IEEE Enterprise Distributed Object Computing Conference (EDOC), IEEE Computer Society, 2004.

[SIV06] S. Sivavakeesar, O.F. Gonzalez, and G. Pavlou, “Service Discovery Strategies in Ubiquitous Communication Environments,” IEEE Communication Magazine, vo1. 44, Issue 9, pp. 106-113, 2006.

[SHE05] X. Shen, W. Zhuang, H. Jiang, and J. Cai, “Medium Access Control in Ultrawideband Wireless Network,” IEEE Transactions on Vehicular Technology, vo1. 54, Issue 5, pp. 1663-1677, 2005.

[SMI05] J. R. Smith, K. P. Fishkin, B. Jiang, A. Mamishev, M. Philipose, A. D. Rea, S. Roy, and K. Sundara-Rajan, “RFID-Based Techniques for Human-Activity Detection,” Communications of the ACM, vol. 48, pp. 39-44, 2005.

[SAL04] A. Salovaara and A. Oulasvirta, “Six Modes of Proactive Resource Management: A User-Centric Typology for Proactive Behaviors,” In Proceedings of the Third Nordic Conference on Human-Computer Interaction, vol. 82, pp. 57-60, 2004.

[SEM] Semantic Markup for Web Services, Available: http://www.w3.org/Submission/OWL-S/.

[SHI06] B. Shi, X. Tao, and J. Lu, “Rewards-Based Negotiation for Providing Context Information,” in Proceedings of the 4th International Workshop on Middleware for Pervasive and Ad-Hoc Computing, 2006.

[SMA02] A. Smailagic, D. P. Siewiorek, J. Anhalt, D. Kogan, and Y. Wang, “Location Sensing and Privacy in a Context-Aware Computing Environment,” IEEE Wireless Communications, vol. 9, pp. 10-17, 2002.

[SRI96] R. Srikant, and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” In Proceedings Of 5th International Conference on Extending Database Technology, EDBT, vol. 1057. pp. 3-17, 1996.

[SU08] W. T. Su, I. H. Liao, K. R. Lee, and Y. H. Kuo, “Service-Oriented Device Composition in Resource-Constrained Ubiquitous Environments,” IEEE Wireless Communications and Networking Conference (WCNC 2008), vol. 1-7, pp. 3110-3115, Mar. 2008.

[SUI08] Y. Sui, F. Shao, R. Sun, and J. Wang, “A Sequential Pattern Mining Algorithm based on Improved FP-Tree,” In Proceedings of the 2008 Ninth ACIS International Conference, 2008.

[TEN00] D. Tennenhouse, “Proactive Computing,” Communication ACM, vol. 43, no. 5, pp. 43-50, 2000.

[UNI] Universal Plug and Play (UPnP), Available: http://www.upnp.org/.

[VAI08] A. Vainio, M. Valtonen, and J. Vanhala, “Proactive Fuzzy Control and Adaptation Methods for Smart Homes,” IEEE Intelligent Systems, vol. 23, no. 2, pp. 42-49, 2008.

[WAL04] P. Walsh and P. Fenton, “A High-Throughput Computing Environment for Job Shop Scheduling (JSP) Genetic Algorithms,” Congress on Evolutionary Computation (CEC), vol. 2, pp. 1554-1560, 2004.

[WAN03] R. Want, T. Pering, and D. Tennenhouse, “Comparing Autonomic and Proactive Computing,” IBM Systems Journal, vol. 42, no. 1, pp. 129-135, 2003.

[WAN04] X. Wang, J. S. Dong, C. Chin, S. Hettiarachchi, and D. Zhang, “Semantic Space: An Infrastructure for Smart Spaces,” IEEE Pervasive Computing, vol. 3, no. 3, pp. 32-39, July-September 2004.

[WU09] B. Wu, C. H. Chi, Z. Chen, et al., “Workflow-Based Resource Allocation to Optimize Overall Performance of Composite Services,” Future Generation Computer System, vol. 25, no. 3, pp. 199-212, 2009.

[WAR06] J. A. Ward, P. Lukowicz, G. Troster, and T. E. Starner, “Activity Recognition of Assembly Tasks Using Body-Worn Microphones and Accelerometers,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, pp. 1553-1567, 2006.

[WAN07] S. Wang, W. Pentney, A. M. Popescu, T. Choudhury, and M. Philipose, “Common Sense Based Joint Training of Human Activity Recognizers,” In Proceedings of International Joint Conference on Artificial Intelligence, pp. 2237-2242, Hyderabad, India, 2007.

[WYA05] D. Wyatt, M. Philipose, and T. Choudhury, “Unsupervised Activity Recognition Using Automatically Mined Common Sense,” In Proceedings of International Conference on AAAI, pp. 21-27, Pittsburgh, Pennsylvania, USA, 2005.

[WU02] H. Wu, M. Siegel, and S. Ablay, “Sensor Fusion for Context Understanding,” In Proceedings of IEEE Instrumentation and Measurement Technology Conference, pp. 13-17, Anchorage, USA, May 2002.

[XIA02] L. Xiao, S. Chen, and X. Zhang, “Dynamic Cluster Resource Allocations for Jobs with Known and Unknown Memory Demands,” IEEE Transactions on Parallel and Distributed Systems, vol. 13, no. 3, pp. 223-240, 2002.

[XU05] C. Xu and S. C. Cheung, “Inconsistency Detection and Resolution for Context-Aware Middleware Support,” In Proceedings of the 10th European Software Engineering Conference held jointly with 13th ACM SIGSOFT International Symposium on Foundations of Software Engineering, Lisbon, Portugal, Sept. 05-09, 2005.

[YE06] Y. H. Ye and Y. H. Kuo, “Development of a Multi-agent Software Platform for Context-Aware Digital Home Applications and Its Environment Simulator,” Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan, ROC, 2006.

[YUE07] K. Yue, W. Liu, and W. Li, “Towards Web Services Composition based on the Mining and Reasoning of Their Causal Relationship,” APWeb/WAIM 2007, LNCS 4505, pp. 777–784, 2007.

[ZAK01] M. J. Zaki, “SPADE: An Efficient Algorithm for Mining Frequent Sequences,” Machine Learning vol. 42, no. 1/2, pp. 31-60, 2001.

[ZHA03] Q. Zhao and S. S. Bhowmick, “Sequential Pattern Mining: A Survey,” Technical Report, CAIS, Nanyang Technological University, Singapore, no. 2003118, 2003.