A comparative analysis of methodologies for database schema integration
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ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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Generic Schema Matching with Cupid
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The VLDB Journal — The International Journal on Very Large Data Bases
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ICDE '04 Proceedings of the 20th International Conference on Data Engineering
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science)
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ACM SIGMOD Record
A framework for modeling and evaluating automatic semantic reconciliation
The VLDB Journal — The International Journal on Very Large Data Bases
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VLDB '05 Proceedings of the 31st international conference on Very large data bases
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ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
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eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
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Information Processing and Management: an International Journal
A composite approach to automating direct and indirect schema mappings
Information Systems
Rank Aggregation for Automatic Schema Matching
IEEE Transactions on Knowledge and Data Engineering
Quasi-inverses of schema mappings
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COMA: a system for flexible combination of schema matching approaches
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ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
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Performance oriented schema matching
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Rule-based construction of matching processes
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The VLDB Journal — The International Journal on Very Large Data Bases
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Schema matching is the task of providing correspondences between concepts describing the meaning of data in various heterogeneous, distributed data sources. It is recognized to be one of the basic operations required by the process of data and schema integration and its outcome serves in many tasks such as targeted content delivery and view integration. Schema matching research has been going on for more than 25 years now. An interesting research topic, that was largely left untouched involves the automatic selection of schema matchers to an ensemble, a set of schema matchers. To the best of our knowledge, none of the existing algorithmic solutions offer such a selection feature. In this paper we provide a thorough investigation of this research topic. We introduce a new heuristic, Schema Matcher Boosting (SMB). We show that SMB has the ability to choose among schema matchers and to tune their importance. As such, SMB introduces a new promise for schema matcher designers. Instead of trying to design a perfect schema matcher, a designer can instead focus on finding better than random schema matchers. For the effective utilization of SMB, we propose a complementary approach to the design of new schema matchers. We separate schema matchers into first-line and second-line matchers. First-line schema matchers were designed by-and-large as applications of existing works in other areas (e.g., machine learning and information retrieval) to schemata. Second-line schema matchers operate on the outcome of other schema matchers to improve their original outcome. SMB selects matcher pairs, where each pair contains a first-line matcher and a second-line matcher. We run a thorough set of experiments to analyze SMB ability to effectively choose schema matchers and show that SMB performs better than other, state-of-the-art ensemble matchers.