Expediting assessments of database performance for streams of respiratory parameters

A new high performance methodology is proposed to compare database performance. The use case chosen is processing of streams of patient respiratory data from patients in an intensive care unit. In general, this application reflects the characteristics of any Internet of Things application where data streams continuously into the system and where analysis of the data is performed at set intervals of time.

New metrics are proposed through which databases may be compared both for this and similar streaming applications. The statistical technique of nonparametric bootstrapping is used to minimise the total running time of the tests. We report mean values and bias corrected and accelerated confidence intervals for each metric and use these to compare the databases. Comparing the non-parametric bootstrapping method to a complete set of tests shows that the two approaches give results differing by a few percent.