You are here

Metadata mapping and reuse in caBIG.

Submitted by holger on Wed, 2013/07/10 - 11:57
TitleMetadata mapping and reuse in caBIG.
Publication TypeJournal Article
Year of Publication2009
AuthorsKunz, I, Lin, M-C, Frey, L
JournalBMC Bioinformatics
Volume10 Suppl 2
Date Published2009
KeywordsAlgorithms, Computational Biology, Database Management Systems, Databases, Factual, Medical Informatics, Software, User-Computer Interface

BACKGROUND: This paper proposes that interoperability across biomedical databases can be improved by utilizing a repository of Common Data Elements (CDEs), UML model class-attributes and simple lexical algorithms to facilitate the building domain models. This is examined in the context of an existing system, the National Cancer Institute (NCI)'s cancer Biomedical Informatics Grid (caBIG). The goal is to demonstrate the deployment of open source tools that can be used to effectively map models and enable the reuse of existing information objects and CDEs in the development of new models for translational research applications. This effort is intended to help developers reuse appropriate CDEs to enable interoperability of their systems when developing within the caBIG framework or other frameworks that use metadata repositories.RESULTS: The Dice (di-grams) and Dynamic algorithms are compared and both algorithms have similar performance matching UML model class-attributes to CDE class object-property pairs. With algorithms used, the baselines for automatically finding the matches are reasonable for the data models examined. It suggests that automatic mapping of UML models and CDEs is feasible within the caBIG framework and potentially any framework that uses a metadata repository.CONCLUSION: This work opens up the possibility of using mapping algorithms to reduce cost and time required to map local data models to a reference data model such as those used within caBIG. This effort contributes to facilitating the development of interoperable systems within caBIG as well as other metadata frameworks. Such efforts are critical to address the need to develop systems to handle enormous amounts of diverse data that can be leveraged from new biomedical methodologies.

Alternate JournalBMC Bioinformatics
PubMed ID19208192
PubMed Central IDPMC2646244
No votes yet