A Novel Method for Instance Level Schema Matching

Abstract:

nformation integration refers to the problem of merging, coalescing and transforming autonomous heterogeneous data sources into a single global homogeneous database and providing a unified view of these data for future query processing purposes. One of the fundamental operations in the integration process is schema matching, which takes two schemas as input and produces a mapping between the attributes of the two schemas that correspond semantically to each other. Matching techniques can be grouped into two broad categories: schema-level matching and instance-level matching. In schema-level matching, we consider only the properties of schema elements, such as names, descriptions, data types, constraints and structures. For each match candidate pair of attributes, the degree of similarity is estimated by a normalized numeric value between 0 and 1. On the other hand, instance-level matching employs information available in the data contents of each table to determine the relationship between any two attributes. In this paper, we propose a statistical model to compare the likeliness of two lists of values under two attributes from separate databases, in order to derive the similarity ratio of the two attributes. Our framework provides efficient procedures to compute the degree ratio using statistical coefficients for both categorical and numeric attributes.

• Periodical: Advanced Materials Research (Volumes 791-793)
• Pages: 1283-1288
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.791-793.1283
• Online since: September 2013
• Authors: Yu Xiang Han
• Keywords: Data Integration, Instance Level, Schema Matching, Similarity Matching, Statistical Methods

References

[1] C. Batini, M. Lenzerini, and SB. Navathe. A comparative analysis of methodologies for database schema integration. ACM Computing Survey, 18(4): 323-364, (1986).

DOI: 10.1145/27633.27634

[2] Surajit Chaudhuri, Anish Das Sarma, Venkatesh Ganti, and Raghav Kaushik. Leveraging aggregate constraints for deduplication. In Proceedings of ACM SIGMOD-07, Beijing China, (2007).

DOI: 10.1145/1247480.1247530

[3] William W. Cohen. Integration of heterogeneous databases without common domains using queries based on textual similarity. In Proceedings of ACM SIGMOD-98, Seattle, WA, (1998).

DOI: 10.1145/276304.276323

[4] Todd J. Green, Grigoris Karvounarakis, Zachary G. Ives, and Val Tannen. Update Exchange with Mappings and Provenance. In Proceedings of VLDB 2007, Vienna, Austria.

[5] G Iversen, M. Gergen. Statistics: The Conceptual Approach. © 1997 Springer-Verlag New York, Inc.

[6] Zoubida Kedad, Elisabeth Métais. Dealing with Semantic Heterogeneity During Data Integration. In Jacky Akoka, Mokrane Bouzeghoub, Isabelle Comyn Wattiau, and Elisabeth M'etais, editors, 18th International Conference on Conceptual Modeling, volume 1, pages 325-339, Paris, 1999. Springer.

DOI: 10.1007/3-540-47866-3_22

[7] E. -P. Lim, J. Srivastava, and S. Shekhar. Resolving Attribute Incompatibility in Database Integration: An Evidential Reasoning Approach. In Proc. of the 10th IEEE Int. Conf. on Data Engineering, ICDE'94, Houston, Texas, USA, 14-18 February (1994).

DOI: 10.1109/icde.1994.283022

[8] M. Liu, T. W. Ling, and T. Guan. Integration of Semistructured Data with Partial and Inconsistent Information. In Proceedings of the International Database Engineering and Application Symposium (IDEAS '99), pages 44-52, Montreal, Canada, August 2-4 1999. IEEE-CS Press.

DOI: 10.1109/ideas.1999.787250

[9] R.J. Miller, M.A. Hernandez, L.M. Haas, L. -L. Yan, C.T.H. Ho, R. Fagin, and L. Popa. The Clio project: Managing heterogeneity. SIGMOD Record, 30(1): 78- 83, (2001).

DOI: 10.1145/373626.373713

[10] Yan Qi, K. Selçuk Candan, and Maria Luisa Sapino. FICSR: feedback-based inconsistency resolution and query processing on misaligned data sources. In Proceedings of ACM SIGMOD-07, Beijing China, (2007).

DOI: 10.1145/1247480.1247499

[11] E. Rahm, P.A. Bernstein: A Survey of Approaches to Automatic Schema Matching. VLDB Journal 10: 4, (2001).

DOI: 10.1007/s007780100057

[12] D. Turnbull, L. Barrington, and G. Lanck-riet. Towards musical query-by-semantic description using the CAL500 data set. In Proceedings of SIGIR '07, (2007).

DOI: 10.1145/1277741.1277817