MiniCon: A scalable algorithm for answering queries using views
The VLDB Journal — The International Journal on Very Large Data Bases
Preserving mapping consistency under schema changes
The VLDB Journal — The International Journal on Very Large Data Bases
Representing and Querying Data Transformations
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Composing schema mappings: Second-order dependencies to the rescue
ACM Transactions on Database Systems (TODS) - Special Issue: SIGMOD/PODS 2004
Query reformulation with constraints
ACM SIGMOD Record
From SPARQL to rules (and back)
Proceedings of the 16th international conference on World Wide Web
(Semantic web) evolution through change logs: problems and solutions
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Composing mappings among data sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Ontology change: Classification and survey
The Knowledge Engineering Review
Datalog±: a unified approach to ontologies and integrity constraints
Proceedings of the 12th International Conference on Database Theory
On the foundations of computing deltas between RDF models
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Ontology change detection using a version log
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
A versioning management model for ontology-based data warehouses
DaWaK'06 Proceedings of the 8th international conference on Data Warehousing and Knowledge Discovery
Scalable query rewriting: a graph-based approach
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Hi-index | 0.00 |
Due to the rapid scientific development, ontologies and schemata need to change. When ontologies evolve, the changes should somehow be rendered and used by the pre-existing data integration systems, a problem that most of the integration systems available today seem to ignore. In this paper, we propose a data integration system that enables and exploits ontology evolution. We redefine data integration under ontology evolution and we show how to describe ontology evolution using logs. Then, we provide the algorithms for rewriting queries among different ontology versions and we present an algorithm based on MiniCon that uses these rewritings and that is guaranteed to find the set of maximally-contained rewritings for the sources. Our extension of the MiniCon algorithm does not involve a significant increase in computational complexity and remains scalable.