Benchmarking a distributed database design that supports patient cohort identification

Abstract

In this article we present the implementation and benchmarking of a medical information system on top of a distributed relational database system. We enhanced a distributed database system with the implementation of a clustering (based on similarity of disease terms) that induces a primary horizontal fragmentation of a data table and derived fragmentations of secondary tables. With our clustering-based fragmentation, data locality for similarity-based query answering is ensured so that data do not have to be sent unnecessarily over the network. In our benchmark we show that we achieve a significant efficiency gain when retrieving all relevant related answers.