Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Database intergration using neural networks: implementation and experiences
Knowledge and Information Systems
Generic Schema Matching with Cupid
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
Rondo: a programming platform for generic model management
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Learning to map between structured representations of data
Learning to map between structured representations of data
Matching large schemas: Approaches and evaluation
Information Systems
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
An XML Schema integration and query mechanism system
Data & Knowledge Engineering
PORSCHE: Performance ORiented SCHEma mediation
Information Systems
Semantic matching: algorithms and implementation
Journal on data semantics IX
A survey of schema-based matching approaches
Journal on Data Semantics IV
Double-layered schema integration of heterogeneous XML sources
Journal of Systems and Software
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This paper presents a novel schema mediation approach, called XMiner, for mining mediated schemas from a set of XML schemas. XMiner addresses three main problems resulting from the heterogeneous source schemas: nesting discrepancy, backward paths and schema discrepancy. XMiner discovers frequent substructures using frequent subtree mining algorithms, and then constructs a mediated schemas. XMiner aims to preserve the hierarchical structure as the best as possible while avoiding information loss. XMiner exploits structural context, forward/backward paths, and label semantics for matching, mapping and merging frequent substructures. Experiments on real and synthetic datasets are reported to show that XMiner offers acceptable performance and quality for large-scale application scenarios.