ACM Transactions on Database Systems (TODS)
Language features for interoperability of databases with schematic discrepancies
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
HILOG: a foundation for higher-order logic programming
Journal of Logic Programming
Using schematically heterogeneous structures
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Data integration: a theoretical perspective
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Foundations of Databases: The Logical Level
Foundations of Databases: The Logical Level
Query languages for relational multidatabases
The VLDB Journal — The International Journal on Very Large Data Bases
Schema Mapping as Query Discovery
VLDB '00 Proceedings of the 26th 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
Data exchange: getting to the core
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Ontology mapping: the state of the art
The Knowledge Engineering Review
Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science)
Generic Model Management: Concepts And Algorithms (Lecture Notes in Computer Science)
Relational languages for metadata integration
ACM Transactions on Database Systems (TODS)
Mapping Between Data Sources on the Web
WIRI '05 Proceedings of the International Workshop on Challenges in Web Information Retrieval and Integration
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Technologies for overcoming heterogeneities between autonomous data sources are key in the emerging networked world. In this paper we discuss the initial results of a formal investigation into the underpinnings of technologies for alleviating structural heterogeneity. At the core of structural heterogeneity is the data mapping problem: discovering effective mappings between structured representations of data. Automating the discovery of these mappings is one of the fundamental unsolved challenges for data interoperability, integration, and sharing. We introduce a novel data model and calculus for expressing data mappings between relational data sources, laying the ground for a better understanding of the data mapping problem. This research uncovers several new safety issues in data mapping languages. We discuss ongoing investigations of syntactic and semantic restrictions on the calculus to deal with these issues.