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Benchmarking a distributed database design that supports patient cohort identification

: Schäfer, J.M.; Sax, U.; Wiese, L.


Desai, B.P.:
24th International Database Engineering and Applications Symposium, IDEAS 2020 : Seoul, Republic of Korea, August, 2020
New York: ACM, 2020
ISBN: 978-1-4503-7503-0
Art. 18, 8 pp.
International Database Engineering and Applications Symposium (IDEAS) <24, 2020, Seoul>
Conference Paper
Fraunhofer ITEM ()

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.