A comparative analysis of methodologies for database schema integration
ACM Computing Surveys (CSUR)
View Integration: A Step Forward in Solving Structural Conflicts
IEEE Transactions on Knowledge and Data Engineering
Comparing Images Using the Hausdorff Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Database Systems: A Practical Approach to Design, Implementation and Management (4th Edition)
Database Systems: A Practical Approach to Design, Implementation and Management (4th Edition)
Leveraging data and structure in ontology integration
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
Schema merging and mapping creation for relational sources
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Interactive generation of integrated schemas
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
FCA-MERGE: bottom-up merging of ontologies
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Toward automated large-scale information integration and discovery
Data Management in a Connected World
Managing uncertainty in schema matching with top-k schema mappings
Journal on Data Semantics VI
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
Discovering implicit categorical semantics for schema matching
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Efficient early top-k query processing in overloaded P2P systems
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
Merging relational views: a minimization approach
ER'11 Proceedings of the 30th international conference on Conceptual modeling
Ontology guided data linkage framework for discovering meaningful data facts
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Data & Knowledge Engineering
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 and based on a set of correspondences that are the result of matching the source schemas. Previous methods for schema integration rely on the exploration, implicit or explicit, of the multiple design choices that are possible for the integrated schema. Such exploration relies heavily on user interaction; thus, it is time consuming and labor intensive. Furthermore, previous methods have ignored the additional information that typically results from the schema matching process, that is, the weights and in some cases the directions that are associated with the correspondences. In this paper, we propose a more automatic approach to schema integration that is based on the use of directed and weighted correspondences between the concepts that appear in the source schemas. A key component of our approach is a novel top-k ranking algorithm for the automatic generation of the best candidate schemas. The algorithm gives more weight to schemas that combine the concepts with higher similarity or coverage. Thus, the algorithm makes certain decisions that otherwise would likely be taken by a human expert. We show that the algorithm runs in polynomial time and moreover has good performance in practice.