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 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.

Citing LifeStream

@inproceedings{10.1145/3445814.3446725,
     author = {Jayarajan, Anand and Hau, Kimberly and Goodwin, Andrew and Pekhimenko, Gennady},
     title = {LifeStream: A High-Performance Stream Processing Engine for Periodic Streams},
     year = {2021},
     isbn = {9781450383172},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3445814.3446725},
     doi = {10.1145/3445814.3446725},
     booktitle = {Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems},
     pages = {107–122},
     numpages = {16},
     keywords = {physiological data, locality tracing, temporal query processing, event lineage tracking, stream data analytics, targeted query processing},
     location = {Virtual, USA},
     series = {ASPLOS 2021}
}