  Hospitals around the world collect massive amounts of physiological data from
their patients every day. Recently, there has been an increase in research
interest to subject this data to statistical analysis to gain more insights and
provide improved medical diagnoses. Such analyses require complex computations
on large volumes of data, demanding efficient data processing systems. This
paper shows that currently available data processing solutions either fail to
meet the performance requirements or lack simple and flexible programming
interfaces. To address this problem, we propose \emph{LifeStream}, a
high-performance stream processing engine for physiological data. LifeStream
hits the sweet spot between ease of programming by providing a rich temporal
query language support and performance by employing optimizations that exploit
the periodic nature of physiological data. We demonstrate that LifeStream
achieves end-to-end performance up to $7.5\times$ higher than state-of-the-art
streaming engines and $3.2\times$ than hand-optimized numerical libraries on
real-world datasets and workloads.
