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

Justin Schoenit; Seth Akins; Ramon Lawrence

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Paper

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

Topics: Internet of Things; Non-Relational Databases; Performance Evaluation and Benchmarking; Query Languages and Query Processing

In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS, 240-249, 2024 , Angers, France

Authors: Justin Schoenit ; Seth Akins and Ramon Lawrence

Affiliation: University of British Columbia, Kelowna, BC, Canada

Keyword(s): Database, Key-Value Store, Embedded, Arduino, Internet of Things, SQL, Time Series, SQLite.

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.

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Paper citation in several formats:

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; ISSN 2184-4992, SciTePress, pages 240-249. DOI: 10.5220/0012558100003690

@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},
issn={2184-4992},
}

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
IS - 2184-4992
AU - Schoenit, J.
AU - Akins, S.
AU - Lawrence, R.
PY - 2024
SP - 240
EP - 249
DO - 10.5220/0012558100003690
PB - SciTePress