A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
On generating all maximal independent sets
Information Processing Letters
Data integration: a theoretical perspective
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Foundations of Databases: The Logical Level
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View Integration: A Step Forward in Solving Structural Conflicts
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Theoretical Aspects of Schema Merging
EDBT '92 Proceedings of the 3rd International Conference on Extending Database Technology: Advances in Database Technology
The Use of Information Capacity in Schema Integration and Translation
VLDB '93 Proceedings of the 19th International Conference on Very Large Data Bases
PROMPT: Algorithm and Tool for Automated Ontology Merging and Alignment
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Proceedings of the 2007 ACM SIGMOD international conference on Management of data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Merging models based on given correspondences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Semi-automatic schema integration in Clio
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
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EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
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IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently incorporating user feedback into information extraction and integration programs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Top-k generation of integrated schemas based on directed and weighted correspondences
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
A gauss function based approach for unbalanced ontology matching
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Data Modeling in Dataspace Support Platforms
Conceptual Modeling: Foundations and Applications
Semi-automated schema integration with SASMINT
Knowledge and Information Systems
Automatic schema merging using mapping constraints among incomplete sources
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Top-k generation of mediated schemas over multiple data sources
DASFAA'10 Proceedings of the 15th international conference on Database systems for advanced applications
Automatic multi-schema integration based on user preference
WAIM'10 Proceedings of the 11th international conference on Web-age information management
Double-layered schema integration of heterogeneous XML sources
Journal of Systems and Software
Schema label normalization for improving schema matching
Data & Knowledge Engineering
Wrangler: interactive visual specification of data transformation scripts
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Data collection by the people, for the people
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Merging relational views: a minimization approach
ER'11 Proceedings of the 30th international conference on Conceptual modeling
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Transactions on Large-Scale Data- and Knowledge-Centered Systems V
Information Visualization - Special issue on State of the Field and New Research Directions
Target-driven merging of taxonomies with Atom
Information Systems
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Schema integration is the problem of creating a unified target schema based on a set of existing source schemas that relate to each other via specified correspondences. The unified schema gives a standard representation of the data, thus offering a way to deal with the heterogeneity in the sources. In this paper, we develop a method and a design tool that provide: 1) adaptive enumeration of multiple interesting integrated schemas, and 2) easy-to-use capabilities for refining the enumerated schemas via user interaction. Our method is a departure from previous approaches to schema integration, which do not offer a systematic exploration of the possible integrated schemas. The method operates at a logical level, where we recast each source schema into a graph of concepts with Has-A relationships. We then identify matching concepts in different graphs by taking into account the correspondences between their attributes. For every pair of matching concepts, we have two choices: merge them into one integrated concept or keep them as separate concepts. We develop an algorithm that can systematically output, without duplication, all possible integrated schemas resulting from the previous choices. For each integrated schema, the algorithm also generates a mapping from the source schemas to the integrated schema that has precise information-preserving properties. Furthermore, we avoid a full enumeration, by allowing users to specify constraints on the merging process, based on the schemas produced so far. These constraints are then incorporated in the enumeration of the subsequent schemas. The result is an adaptive and interactive enumeration method that significantly reduces the space of alternative schemas, and facilitates the selection of the final integrated schema.