Abstract
Schema matching is widely used in many applications, such as data integration, ontology merging, data warehouse and dataspaces. In this paper, we propose a novel matching technique based on the order of attributes appearing in the schema structure of query results. The appearance order embodies the extent of the importance of an attribute for the user examining the query results. The core idea of our approach is to collect the statistics about the appearance order of attributes from the query logs to find correspondences between attributes in the schemas to be matched. As a first step, we employ a matrix to structure the statistics about the appearance order of attributes. Then, two scoring functions are considered to measure the similarity of the collected statistics. Finally, an traditional algorithm is employed to find the mapping with the highest score. Furthermore, our approach can be seen as a complementary member to the family of the existing matchers, and can also be combined with them to obtain more accurate results. We validate our approach with an experimental study, the results of which demonstrate that our approach is effective and has good performance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P.: Optimization by simulated annealing. Science Journal 220, 671–680 (1983)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB Journal 10(4), 334–350 (2001)
Melnik, S., Garcia-Molina, H., Rahm, E.: Similarity flooding: a versatile graph matching algorithm. In: Proc. of ICDE, pp. 117–128 (2002)
Kang, J., Naughton, J.F.: On Schema Matching with Opaque Column Names and Data Values. In: Proc. of SIGMOD, pp. 205–216 (2003)
Franklin, M., Halevy, A., Maier, D.: From Databases to Dataspaces: A New Abstraction for Information Management. In: Proc. of SIGMOD, pp. 1–7 (2005)
Madhavan, J., Bernstein, P., Doan, A., Halevy, A.: Corpus based schema matching. In: Proc. of ICDE, pp. 57–68 (2005)
Halevy, A., Rajaraman, A., Ordille, J.: Data integration: The teenage years. In: Proc. of VLDB, pp. 9–16 (2006)
Bohannon, P., Elnahrawy, E., Fan, W., Flaster, M.: Putting context into schema matching. In: Proc. of VLDB, pp. 307–318 (2006)
Warren, R.H., Tompa, F.: Multicolumn Substring Matching for Database Schema Translation. In: Proc. of VLDB, pp. 331–342 (2006)
Dong, X., Halevy, A.Y., Yu, C.: Data integration with uncertainty. In: Proc. of VLDB, pp. 687–698 (2007)
Chan, C., Elmeleegy, H.V.J.H., Ouzzani, M., Elmagarmid, A.: Usage-Based Schema Matching. In: Proc.of ICDE, pp. 20–29 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ding, G., Dong, H., Wang, G. (2012). Appearance-Order-Based Schema Matching. In: Lee, Sg., Peng, Z., Zhou, X., Moon, YS., Unland, R., Yoo, J. (eds) Database Systems for Advanced Applications. DASFAA 2012. Lecture Notes in Computer Science, vol 7238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29038-1_8
Download citation
DOI: https://doi.org/10.1007/978-3-642-29038-1_8
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29037-4
Online ISBN: 978-3-642-29038-1
eBook Packages: Computer ScienceComputer Science (R0)