ACM Computing Surveys (CSUR)
Generic Schema Matching with Cupid
Proceedings of the 27th International Conference on Very Large Data Bases
Approximate String Joins in a Database (Almost) for Free
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
Similarity Flooding: A Versatile Graph Matching Algorithm and Its Application to Schema Matching
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Software—Practice & Experience
The PROMPT suite: interactive tools for ontology merging and mapping
International Journal of Human-Computer Studies
Industrial-strength schema matching
ACM SIGMOD Record
Bootstrapping ontology alignment methods with APFEL
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Using Element Clustering to Increase the Efficiency of XML Schema Matching
ICDEW '06 Proceedings of the 22nd International Conference on Data Engineering Workshops
eTuner: tuning schema matching software using synthetic scenarios
The VLDB Journal — The International Journal on Very Large Data Bases
Ontology Matching
Matching large schemas: Approaches and evaluation
Information Systems
Model management 2.0: manipulating richer mappings
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
COMA: a system for flexible combination of schema matching approaches
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Quickmig: automatic schema matching for data migration projects
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Scalable semantic web data management using vertical partitioning
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
PORSCHE: Performance ORiented SCHEma mediation
Information Systems
Matching large ontologies: A divide-and-conquer approach
Data & Knowledge Engineering
Falcon-AO: A practical ontology matching system
Web Semantics: Science, Services and Agents on the World Wide Web
Poster Session: An Indexing Structure for Automatic Schema Matching
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
RiMOM: A Dynamic Multistrategy Ontology Alignment Framework
IEEE Transactions on Knowledge and Data Engineering
Ontology matching with semantic verification
Web Semantics: Science, Services and Agents on the World Wide Web
A method for recommending ontology alignment strategies
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
A survey of schema-based matching approaches
Journal on Data Semantics IV
On matching large life science ontologies in parallel
DILS'10 Proceedings of the 7th international conference on Data integration in the life sciences
A clustering-based approach for large-scale ontology matching
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
Constructing virtual documents for ontology matching using mapreduce
JIST'11 Proceedings of the 2011 joint international conference on The Semantic Web
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A recurring manual task in data integration, ontology alignment or model management is finding mappings between complex meta data structures. In order to reduce the manual effort, many matching algorithms for semi-automatically computing mappings were introduced. Unfortunately, current matching systems severely lack performance when matching large schemas. Recently, some systems tried to tackle the performance problem within individual matching approaches. However, none of them developed solutions on the level of matching processes. In this paper we introduce a novel rewrite-based optimization technique that is generally applicable to different types of matching processes. We introduce filter-based rewrite rules similar to predicate push-down in query optimization. In addition we introduce a modeling tool and recommendation system for rewriting matching processes. Our evaluation on matching large web service message types shows significant performance improvements without losing the quality of automatically computed results.