Superviews: Virtual Integration of Multiple Databases
IEEE Transactions on Software Engineering
Infomaster: an information integration system
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
The TSIMMIS Approach to Mediation: Data Models and Languages
Journal of Intelligent Information Systems - Special issue: next generation information technologies and systems
Maintaining data warehouses over changing information sources
Communications of the ACM
Reconciling schemas of disparate data sources: a machine-learning approach
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Schema Mapping as Query Discovery
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Querying Heterogeneous Information Sources Using Source Descriptions
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Database Schema Matching Using Machine Learning with Feature Selection
CAiSE '02 Proceedings of the 14th International Conference on Advanced Information Systems Engineering
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Utility-based resolution of data inconsistencies
Proceedings of the 2004 international workshop on Information quality in information systems
The case for mesodata: An empirical investigation of an evolving database system
Information and Software Technology
ACM Computing Surveys (CSUR)
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We describe three generations of information integration systems developed at George Mason University. All three systems adopt a virtual database design: a global integration schema, a mapping between this schema and the schemas of the participating information sources, and automatic interpretation of global queries. The focus of Multiplex is rapid integration of very large, evolving, and heterogeneous collections of information sources. Fusionplex strengthens these capabilities with powerful tools for resolving data inconsistencies. Finally, Autoplex takes a more proactive approach to integration, by "recruiting" contributions to the global integration schema from available information sources. Using machine learning techniques it confronts a major cost of integration, that of mapping new sources into the global schema.