Matching large XML schemas

ABSTRACT

Current schema matching approaches still have to improve for very large and complex schemas. Such schemas are increasingly written in the standard language W3C XML schema, especially in E-business applications. The high expressive power and versatility of this schema language, in particular its type system and support for distributed schemas and name-spaces, introduce new issues. In this paper, we study some of the important problems in matching such large XML schemas. We propose a fragment-oriented match approach to decompose a large match problem into several smaller ones and to reuse previous match results at the level of schema fragments.

REFERENCES

• 1: Agnar Aamodt , Enric Plaza, Case-based reasoning: foundational issues, methodological variations, and system approaches, AI Communications, v.7 n.1, p.39-59, March 1994
• 2: S. Bergamaschi , S. Castano , M. Vincini, Semantic integration of semistructured and structured data sources, ACM SIGMOD Record, v.28 n.1, p.54-59, March 1999 [doi>10.1145/309844.309897]
• 3: Jacob Berlin , Amihai Motro, Autoplex: Automated Discovery of Content for Virtual Databases, Proceedings of the 9th International Conference on Cooperative Information Systems, p.108-122, September 05-07, 2001
• 4: Philip A. Bernstein , Sergey Melnik , Michalis Petropoulos , Christoph Quix, Industrial-strength schema matching, ACM SIGMOD Record, v.33 n.4, December 2004 [doi>10.1145/1041410.1041417]
• 5: Do, H. H., E. Rahm: COMA - A System for Flexible Combination of Match Algorithms. VLDB 2002
• 6: Hong Hai Do , Sergey Melnik , Erhard Rahm, Comparison of Schema Matching Evaluations, Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems, p.221-237, October 07-10, 2002
• 7: 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
• 8: 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]
• 9: Madhavan, J. et al.: Corpus-based Schema Matching. Workshop on Information Integration on the Web (IIWeb), 2003
• 10: 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
• 11: Melnik, S., H. Garcia-Molina, E. Rahm: Similarity Flooding - A Versatile Graph Matching Algorithm. ICDE 2002
• 12: Peter Mork , Philip A. Bernstein, Adapting a Generic Match Algorithm to Align Ontologies of Human Anatomy, Proceedings of the 20th International Conference on Data Engineering, p.787, March 30-April 02, 2004
• 13: Naumann, F. et al.: Attribute Classification Using Feature Analysis. ICDE 2002
• 14: Luigi Palopoli , Domenico Saccà , Giorgio Terracina , Domenico Ursino, Uniform Techniques for Deriving Similarities of Objects and Subschemes in Heterogeneous Databases, IEEE Transactions on Knowledge and Data Engineering, v.15 n.2, p.271-294, February 2003 [doi>10.1109/TKDE.2003.1185834]
• 15: 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]
• 16: XML Schemas - Best Practices. <u>www.xfront.com/BestPracticesHomepage.html</u>