Schema matching for context-aware computing

ABSTRACT

Context-aware computing is a key paradigm of ubiquitous computing in which applications automatically adapt their operations to dynamic context data from multiple sources. Managing a number of distributed sources, a middleware that facilitates the development of context-aware applications must provide a uniform view of all these sources to the applications. Local schemas of context data from individual sources need to be matched into a set of global schemas in the middleware, upon which applications can issue context queries to acquire data. In this paper, we study this problem of schema matching for context-aware computing. We propose a multi-criteria algorithm to determine candidate attribute matches between two schemas. The algorithm adaptively adjusts the priorities of different criteria based on previous matching results to improve the efficiency and accuracy of succeeding operations. We further develop an algorithm to categorize a new local schema into one of the global schemas whenever possible via a shared attribute dictionary. Our results based on schemas from real-world websites demonstrate the good matching accuracy achieved by our algorithms.

REFERENCES

• 1: Guanling Chen , David Kotz, A Survey of Context-Aware Mobile Computing Research, Dartmouth College, Hanover, NH, 2000
• 2: Anind K. Dey, Understanding and Using Context, Personal and Ubiquitous Computing, v.5 n.1, p.4-7, February 2001 [doi>10.1007/s007790170019]
• 3: AnHai Doan , Pedro Domingos , Alon Y. Halevy, Reconciling schemas of disparate data sources: a machine-learning approach, Proceedings of the 2001 ACM SIGMOD international conference on Management of data, p.509-520, May 21-24, 2001, Santa Barbara, California, United States
• 4: Tao Gu , Hung Keng Pung , Da Qing Zhang, A service-oriented middleware for building context-aware services, Journal of Network and Computer Applications, v.28 n.1, p.1-18, January 2005 [doi>10.1016/j.jnca.2004.06.002]
• 5: Pari Delir Haghighi , Arkady Zaslavsky , Shonali Krishnaswamy, An Evaluation of Query Languages for Context-Aware Computing, Proceedings of the 17th International Conference on Database and Expert Systems Applications, p.455-462, September 04-08, 2006 [doi>10.1109/DEXA.2006.25]
• 6: Glenn Judd , Peter Steenkiste, Providing Contextual Information to Pervasive Computing Applications, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, p.133, March 23-26, 2003
• 7: Kristian Ellebæk Kjær, A survey of context-aware middleware, Proceedings of the 25th conference on IASTED International Multi-Conference: Software Engineering, p.148-155, February 13-15, 2007, Innsbruck, Austria
• 8: Landauer, T., Foltz, P. W. and Laham, D. Introduction to Latent Semantic Analysis. Discourse Processes 25 (1998), 259--284.
• 9: Wen-Syan Li , Chris Clifton, SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks, Data & Knowledge Engineering, v.33 n.1, p.49-84, April 2000 [doi>10.1016/S0169-023X(99)00044-0]
• 10: Longest Common Substring Problem. http://en.wikipedia.org/wiki/Longest_common_substring_problem.
• 11: Samuel R. Madden , Michael J. Franklin , Joseph M. Hellerstein , Wei Hong, TinyDB: an acquisitional query processing system for sensor networks, ACM Transactions on Database Systems (TODS), v.30 n.1, p.122-173, March 2005 [doi>10.1145/1061318.1061322]
• 12: Jayant Madhavan , Philip A. Bernstein , Erhard Rahm, Generic Schema Matching with Cupid, Proceedings of the 27th International Conference on Very Large Data Bases, p.49-58, September 11-14, 2001
• 13: Ng, W. S., Ooi, B. C., Tan, K. L. and Zhou, A. PeerDB: A P2P-based System for Distributed Data Sharing. In Proc. SIGMOD 2003, ACM Press (2003), 659.
• 14: Natalya F. Noy, Semantic integration: a survey of ontology-based approaches, ACM SIGMOD Record, v.33 n.4, December 2004 [doi>10.1145/1041410.1041421]
• 15: Ontology Matching. http://www.ontologymatching.org/index.html.
• 16: Porter Stemming Algorithm. http://tartarus.org/~martin/PorterStemmer/.
• 17: Erhard Rahm , Philip A. Bernstein, A survey of approaches to automatic schema matching, The VLDB Journal — The International Journal on Very Large Data Bases, v.10 n.4, p.334-350, December 2001 [doi>10.1007/s007780100057]
• 18: Raghu Ramakrishnan , Johannes Gehrke, Database Management Systems, McGraw-Hill Science/Engineering/Math, 2002
• 19: Stop Word List. http://www.dcs.gla.ac.uk/idom/ir_resources/linguistic_utils/stop_words.
• 20: WordNet. http://wordnet.princeton.edu/.
• 21: Yong Yao , Johannes Gehrke, The cougar approach to in-network query processing in sensor networks, ACM SIGMOD Record, v.31 n.3, September 2002 [doi>10.1145/601858.601861]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.m Miscellaneous

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.1 Logical Design
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval

General Terms:
Algorithms, Design, Management, Performance

Keywords:
context attributes, context awareness, context schemas, middleware, schema matching, ubiquitous computing