Sharing data and knowledge from heterogeneous sources
Environmental information systems in industry and public administration
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
iMAP: discovering complex semantic matches between database schemas
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Transparent access to multiple bioinformatics information sources
IBM Systems Journal - Deep computing for the life sciences
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Reference reconciliation in complex information spaces
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A conceptual modeling and execution framework for process based scientific applications
Proceedings of the ACM first workshop on CyberInfrastructure: information management in eScience
DaltOn: An Infrastructure for Scientific Data Management
ICCS '08 Proceedings of the 8th international conference on Computational Science, Part III
Handbook on Ontologies
Ontology-based data integration in data logistics workflows
ER'07 Proceedings of the 2007 conference on Advances in conceptual modeling: foundations and applications
A survey of schema-based matching approaches
Journal on Data Semantics IV
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Due to the proliferation of data generating devices such as sensors in scientific applications, data integration has become most challenging task since the data stemming from these devices are extremely heterogeneous in terms of structure (schema) and semantics (interpretation). In practice, integration and transformation is typically performed by the scientists manually; in fact extensive efforts are required. The approaches for automating data integration task as much as possible are badly needed. DaltOn is a generic framework that offers various functionalities for managing the data in scientific applications. In this paper, we present DaltOn's functionality for automating data integration task based on exploitation of ontologies. In addition, we also elaborate the specific module of our framework which is responsible for implementing the functionality. At last, we also present core algorithms that demonstrate a good evaluation of our approach.