LifeStream: A High-Performance Stream Processing Engine for Periodic Streams

TL;DR

LifeStream is a high-performance stream processing engine optimized for physiological data, offering a flexible programming interface and achieving significantly higher performance than existing solutions.

Contribution

It introduces LifeStream, a novel stream processing engine that exploits periodic data patterns to enhance performance while maintaining ease of programming.

Findings

Achieves up to 7.5x higher performance than state-of-the-art engines.

Outperforms hand-optimized numerical libraries by 3.2x.

Supports complex temporal queries on physiological data.

Abstract

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.