Understanding the schema matching problem

Authors:
Alsayed Algergawy;Eike Schallehn;Gunter Saake
Affiliations:
Institute for Technical and Business Information Systems, Department of Computer Science, Otto-von-Guericke University, Magdeburg, Germany;Institute for Technical and Business Information Systems, Department of Computer Science, Otto-von-Guericke University, Magdeburg, Germany;Institute for Technical and Business Information Systems, Department of Computer Science, Otto-von-Guericke University, Magdeburg, Germany
Venue:
ACS'07 Proceedings of the 7th Conference on 7th WSEAS International Conference on Applied Computer Science - Volume 7
Year:
2007

Citing 15
Cited 0

SEMINT: a tool for identifying attribute correspondences in heterogeneous databases using neural networks

Data & Knowledge Engineering
Reconciling schemas of disparate data sources: a machine-learning approach

SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Current Approaches for Solving Over-Constrained Problems

Constraints
Current Approaches for Solving Over-Constrained Problems

Constraints
Generic Schema Matching with Cupid

Proceedings of the 27th International Conference on Very Large Data Bases
Comparison of Schema Matching Evaluations

Revised Papers from the NODe 2002 Web and Database-Related Workshops on Web, Web-Services, and Database Systems
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
Learning to map between structured representations of data

Learning to map between structured representations of data
Semantic matching

The Knowledge Engineering Review
Semantic-integration research in the database community

AI Magazine - Special issue on semantic integration
A graph-based approach for extracting terminological properties from information sources with heterogeneous formats

Knowledge and Information Systems
Semantic matching across heterogeneous data sources

Communications of the ACM - The patent holder's dilemma: buy, sell, or troll?
COMA: a system for flexible combination of schema matching approaches

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Graph matching by relaxation of fuzzy assignments

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

Schema matching plays the central role in many applications that require interoperability between heterogeneous data sources. The best way to attain comprehensive understanding of the schema matching problem is to construct a complete, if possible, problem formulation. Schema matching has been intensively researched and many matching systems have been developed. However, specifications of the schema matching problem being solved by these systems do not exist, or if it exists do not take uncertainty problems into account. In this paper, we propose the use of the fuzzy constraint problem (FCP) as a framework to model and understand the schema matching problem. In an effort to achieve more generic approach, we first transform the schema matching problem into a graph matching problem by transforming schemas to be matched into a common model namely rooted labeled graphs. Then, with the aid of this common model, we formulate the graph matching problem into a fuzzy constraint problem. By formalizing the schema matching problem as a FCP, we could express it as a combinatorial problem with soft constraints which enables us dealing with inherent uncertainty in schema matching.