EmbedDB: A High-Performance Time Series Database for Embedded Systems

Justin Schoenit, Seth Akins, Ramon Lawrence

2024

Abstract

Efficient data processing on embedded devices may reduce network communication and improve battery usage allowing for longer sensor lifetime. Data processing is challenged by limited CPU and memory hardware. EmbedDB is a key-value data store supporting time series and relational data on memory-constrained devices. EmbedDB is competitive with SQLite on more powerful embedded hardware such as the Raspberry Pi and executes on hardware such as Arduinos that SQLite and other previous systems cannot. Experimental results evaluating EmbedDB on time series query processing show a speedup of five times compared to SQLite on a Raspberry Pi on many queries, and the ability to execute data processing on small embedded systems not well supported by existing databases.

Paper Citation

in Harvard Style

Schoenit J., Akins S. and Lawrence R. (2024). EmbedDB: A High-Performance Time Series Database for Embedded Systems. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 240-249. DOI: 10.5220/0012558100003690

in Bibtex Style

@conference{iceis24,
author={Justin Schoenit and Seth Akins and Ramon Lawrence},
title={EmbedDB: A High-Performance Time Series Database for Embedded Systems},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={240-249},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012558100003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - EmbedDB: A High-Performance Time Series Database for Embedded Systems
SN - 978-989-758-692-7
AU - Schoenit J.
AU - Akins S.
AU - Lawrence R.
PY - 2024
SP - 240
EP - 249
DO - 10.5220/0012558100003690
PB - SciTePress