A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
Learning to map between ontologies on the semantic web
Proceedings of the 11th international conference on World Wide Web
View Integration: A Step Forward in Solving Structural Conflicts
IEEE Transactions on Knowledge and Data Engineering
Multi-User View Integration System (MUVIS): An Expert System for View Integration
Proceedings of the Sixth International Conference on Data Engineering
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Rondo: a programming platform for generic model management
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A framework for modeling and evaluating automatic semantic reconciliation
The VLDB Journal — The International Journal on Very Large Data Bases
A Probabilistic XML Approach to Data Integration
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Merging models based on given correspondences
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A general approach to the generation of conceptual model transformations
CAiSE'05 Proceedings of the 17th international conference on Advanced Information Systems Engineering
Why is schema matching tough and what can we do about it?
ACM SIGMOD Record
Bootstrapping pay-as-you-go data integration systems
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Preference-Based Uncertain Data Integration
EKAW '08 Proceedings of the 16th international conference on Knowledge Engineering: Practice and Patterns
Optimization of Queries over Interval Probabilistic Data
SUM '08 Proceedings of the 2nd international conference on Scalable Uncertainty Management
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Defining and Using Schematic Correspondences for Automatically Generating Schema Mappings
CAiSE '09 Proceedings of the 21st International Conference on Advanced Information Systems Engineering
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
Data Modeling in Dataspace Support Platforms
Conceptual Modeling: Foundations and Applications
Towards Relational Schema Uncertainty
SUM '09 Proceedings of the 3rd International Conference on Scalable Uncertainty Management
A Survey on Uncertainty Management in Data Integration
Journal of Data and Information Quality (JDIQ)
US-SQL: managing uncertain schemata
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Double-layered schema integration of heterogeneous XML sources
Journal of Systems and Software
Foundations of uncertain-data integration
Proceedings of the VLDB Endowment
Rewriting fuzzy queries using imprecise views
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
A distance function for ontology concepts using extension of attributes' semantics
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Data translation between taxonomies
CAiSE'06 Proceedings of the 18th international conference on Advanced Information Systems Engineering
Schema matching prediction with applications to data source discovery and dynamic ensembling
The VLDB Journal — The International Journal on Very Large Data Bases
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Schema integration is the activity of providing a unified representation of multiple data sources. The core problems in schema integration are: schema matching, i.e. the identification of correspondences, or mappings, between schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Existing schema matching approaches attempt to identify a single mapping between each pair of objects, for which they are 100% certain of its correctness. However, this is impossible in general, thus a human expert always has to validate or modify it. In this paper, we propose a new schema integration approach where the uncertainty in the identified mappings that is inherent in the schema matching process is explicitly represented, and that uncertainty propagates to the schema merging process, and finally it is depicted in the resulting integrated schema.