Formalizing mappings to optimize automated schema alignment: application to rare diseases.

Area of Science:

• Computer Science
• Data Science
• Information Systems

Background:

• Data sharing and systems interoperability necessitate automated data schema alignment.
• Existing alignment approaches rely heavily on data specifications for discovering data mappings.
• Optimizing automated data integration processes is a key challenge.

Purpose of the Study:

• To propose a novel method for formalizing data mappings.
• To enable automated inference of mappings expressed by rules.
• To optimize automated data integration processes.

Main Methods:

• Formalization of data mappings at both data element and value element levels.
• Automated inference of mappings using defined rules.
• Validation through data characterization in two distinct use cases.

Main Results:

• A validated method for data mappings formalization.
• Demonstrated capability for automated inference of mappings.
• Successful characterization of data from two use cases.

Conclusions:

• The proposed formalization method enhances automated data integration.
• Rule-based mapping inference is effective for data element and value levels.
• This approach addresses the priority of data schema alignment in interoperable systems.