一句话：LifeStream 是面向生理数据的流处理引擎，通过时序查询语言与周期性优化，性能显著超越现有系统。
医院每天采集大量生理数据，对这些数据进行统计分析需要在大规模数据上执行复杂计算，因此依赖高效的数据处理系统。本文指出现有数据处理方案要么无法满足性能需求，要么缺乏简单灵活的编程接口。为此，作者提出 LifeStream——一个面向生理数据的高性能流处理引擎，其设计在易编程性与性能之间取得平衡：一方面提供丰富的时序查询语言支持，使开发者能够简洁地表达复杂的时序分析；另一方面利用生理数据固有的周期性特征实施针对性优化。作者在真实数据集与真实工作负载上进行端到端评估，结果显示 LifeStream 的性能最高可达当前最先进流处理引擎的 7.5 倍，比手工优化的数值库快 3.2 倍。
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 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× higher than state-of-the-art streaming engines and 3.2× than hand-optimized numerical libraries on real-world datasets and workloads.
@inproceedings{paperbot6110,
title = {LifeStream: a high-performance stream processing engine for periodic streams},
author = {Anand Jayarajan and Kimberly Hau and Andrew Goodwin and Gennady Pekhimenko},
booktitle = {ASPLOS},
year = {2021},
doi = {10.1145/3445814.3446725}
}