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Data & Knowledge Engineering
The Clio project: managing heterogeneity
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Reconciling schemas of disparate data sources: a machine-learning approach
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Foundations of Databases: The Logical Level
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VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Schema Mapping as Query Discovery
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Generic Schema Matching with Cupid
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SchemaSQL - A Language for Interoperability in Relational Multi-Database Systems
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Physical Data Independence, Constraints, and Optimization with Universal Plans
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A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Semi-Automatic, Semantic Discovery of Properties from Database Schemes
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Rondo: a programming platform for generic model management
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On schema matching with opaque column names and data values
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Extending and inferring functional dependencies in schema transformation
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ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Clio grows up: from research prototype to industrial tool
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Schema and ontology matching with COMA++
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Relational data mapping in MIQIS
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mapping maintenance for data integration systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Mapping adaptation under evolving schemas
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Extending dependencies with conditions
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Schema mapping verification: the spicy way
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Dependencies revisited for improving data quality
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IRI'09 Proceedings of the 10th IEEE international conference on Information Reuse & Integration
Managing XML Schema Mappings and Annotations in P2P Data Integration Systems
OTM '09 Proceedings of the Confederated International Workshops and Posters on On the Move to Meaningful Internet Systems: ADI, CAMS, EI2N, ISDE, IWSSA, MONET, OnToContent, ODIS, ORM, OTM Academy, SWWS, SEMELS, Beyond SAWSDL, and COMBEK 2009
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The VLDB Journal — The International Journal on Very Large Data Bases
Discovering simple mappings between relational database schemas and ontologies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Synthesizing view definitions from data
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Instance-based domain ontological view creation towards semantic integration
Expert Systems with Applications: An International Journal
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Web Semantics: Science, Services and Agents on the World Wide Web
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Knowledge-Based Systems
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Theoretical Computer Science
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Attribute-level schema matching has proven to be an important first step in developing mappings for data exchange, integration, restructuring and schema evolution. In this paper we investigate contextual schema matching, in which selection conditions are associated with matches by the schema matching process in order to improve overall match quality. We define a general space of matching techniques, and within this framework we identify a variety of novel, concrete algorithms for contextual schema matching. Furthermore, we show how common schema mapping techniques can be generalized to take more effective advantage of contextual matches, enabling automatic construction of mappings across certain forms of schema heterogeneity. An experimental study examines a wide variety of quality and performance issues. In addition, it demonstrates that contextual schema matching is an effective and practical technique to further automate the definition of complex data transformations.