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First published October 26, 2006 as JAMIA PrePrint; doi:10.1197/jamia.M2189
Journal of the American Medical Informatics Association 2007;14(1):86-93
© 2007 American Medical Informatics Association

A more recent version of this article appeared on January 1, 2007
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Submitted on June 27, 2006
Accepted on October 5, 2006

Dynamic Tables: An Architecture for Managing Evolving, Heterogeneous Biomedical Data in Relational Database Management Systems

John Corwin1*, Avi Silberschatz PhD1, Perry L. Miller MD, PhD2, and Luis Marenco MD3

Affiliation of the authors: 1 Department of Computer Science, Yale University, New Haven, CT; 2 Center for Medical Informatics, Yale University, New Haven, CT; Department of Anesthesiology, Yale University, New Haven, CT; Department of Molecular, Cellular, and Developmental Biology, Yale University, New Haven, CT ; 3 Center for Medical Informatics, Yale University, New Haven, CT; Department of Anesthesiology, Yale University, New Haven, CT

* To whom correspondence should be addressed.

Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute-value based database schemas to overcome limitations posed when using conventional relational database technology. This paper explores these issues and discusses why biomedical data is difficult to model using conventional relational techniques. We propose a solution to these obstacles based on a relational database engine using a sparse, column-store architecture. We provide benchmarks comparing the performance of queries and schema-modification operations using three different strategies: (1) the standard conventional relational design, (2) past approaches used by biomedical informatics researchers, and (3) our sparse, column-store architecture. The performance results show that our architecture is a promising technique for storing and processing many types of data that are not handled well by the other two semantic data models.






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