A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
On schema matching with opaque column names and data values
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Putting context into schema matching
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Multi-column substring matching for database schema translation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
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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.