A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Statistical schema matching across web query interfaces
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
DBXplorer: A System for Keyword-Based Search over Relational Databases
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Enterprise information integration: successes, challenges and controversies
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Automatic ontology matching using application semantics
AI Magazine - Special issue on semantic integration
Semantic-integration research in the database community
AI Magazine - Special issue on semantic integration
Principles of dataspace systems
Proceedings of the twenty-fifth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Information retrieval and machine learning for probabilistic schema matching
Information Processing and Management: an International Journal
Why is schema matching tough and what can we do about it?
ACM SIGMOD Record
Discover: keyword search in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Data integration with uncertainty
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Interactive generation of integrated schemas
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Pay-as-you-go user feedback for dataspace systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Bootstrapping pay-as-you-go data integration systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Schema integration based on uncertain semantic mappings
ER'05 Proceedings of the 24th international conference on Conceptual Modeling
Search Computing
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Data integration has been an important area of research for several years. However, such systems suffer from one of the main drawbacks of database systems: the need to invest significant modeling effort upfront. Dataspace Support Platforms (DSSP) envision a system that offers useful services on its data without any setup effort, and improve with time in a pay-as-you-go fashion. We argue that in order to support DSSPs, the system needs to model uncertainty at its core. We describe the concepts of probabilistic mediated schemas and probabilistic mappings as enabling concepts for DSSPs.