Cupid System

The Cupid System is a schema matching system that discovers mapping between schema elements and structure.

• AKA: Cupid Schema Matching System.
• Context:
  • It was a generic schema matching system.
  • It discovers matches in schema elements based on their names, data types, constraints and schema structure.
  • …
• Counter-Example(s)
  • COMA, a schema matching system.
  • S-Match, a schema matching system.
  • OLA System, a schema and instance-based matching system.
  • Falcon-AO, a schema and instance-based matching system.
  • RiMOM, a schema and instance-based matching system.
  • ASMOV, a schema and instance-based matching system.
  • LogMap,a schema and instance-based matching system.
  • eTuner, a metamatching system.
• See: Taxonomy, Ontology Matching, Database Schema.

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References

2014

• (Euzenat & Shvaiko, 2014) ⇒ Euzenat, J., & Shvaiko, P. (2014). Ontology matching tutorial, pg. 79-95
  • State of the art systems: 100+ matching systems exist, . . . we consider some of them
    • Cupid (U. of Washington, Microsoft Corporation and U. of Leipzig)
    • S-Match (U. of Trento)
    • OLA (INRIA Rhˆone-Alpes and U. de Montréal)
    • Falcon-AO (China Southwest U.)
    • RiMOM (Tsinghua U.)
    • ASMOV (INFOTECH Soft, Inc., U. of Miami)
    • LogMap (U. of Oxford)
    • eTuner (U. of Illinois and The MITRE Corporation)

2007

• (Euzenat & Shvaiko, 2007) ⇒ Euzenat, J., & Shvaiko, P. (2007). Ontology matching (Vol. 18). Heidelberg: Springer.

2001

• (Madhavan et al., 2001) ⇒ Jayant Madhavan, Philip A. Bernstein, and Erhard Rahm. (2001). “Generic Schema Matching with Cupid.” In: Proceedings of the 27th International Conference on Very Large Data Bases
  • Abstract: Schema matching is a critical step in many applications, such as XML message mapping, data warehouse loading, and schema integration. In this paper, we investigate algorithms for generic schema matching, outside of any particular data model or application. We first present a taxonomy for past solutions, showing that a rich range of techniques is available. We then propose a new algorithm, Cupid, that discovers mappings between schema elements based on their names, data types, constraints, and schema structure, using a broader set of techniques than past approaches. Some of our innovations are the integrated use of linguistic and structural matching, context-dependent matching of shared types, and a bias toward leaf structure where much of the schema content resides. After describing our algorithm, we present experimental results that compare Cupid to two other schema matching systems.

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