Adapting Linear Hashing for Flash Memory Resource-constrained Embedded Devices

Andrew Feltham, Spencer MacBeth, Scott Fazackerley, Ramon Lawrence

Abstract

Linear hashing provides constant time operations for data indexing and has been widely implemented for database systems. Embedded devices, often with limited memory and CPU resources, are increasingly collecting and processing more data and benefit from fast index structures. Implementing linear hashing for flash-based embedded devices is challenging both due to the limited resources and the unique properties of flash memory. In this work, an implementation of linear hashing optimized for embedded devices is presented and evaluated. Experimental results demonstrate that the implementation has constant time performance on embedded devices, even with as little as 8 KB of memory, and offers benefits for several use cases.

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Paper Citation


in Harvard Style

Feltham A., MacBeth S., Fazackerley S. and Lawrence R. (2019). Adapting Linear Hashing for Flash Memory Resource-constrained Embedded Devices.In Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-372-8, pages 176-181. DOI: 10.5220/0007709301760181


in Bibtex Style

@conference{iceis19,
author={Andrew Feltham and Spencer MacBeth and Scott Fazackerley and Ramon Lawrence},
title={Adapting Linear Hashing for Flash Memory Resource-constrained Embedded Devices},
booktitle={Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2019},
pages={176-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007709301760181},
isbn={978-989-758-372-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 21st International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Adapting Linear Hashing for Flash Memory Resource-constrained Embedded Devices
SN - 978-989-758-372-8
AU - Feltham A.
AU - MacBeth S.
AU - Fazackerley S.
AU - Lawrence R.
PY - 2019
SP - 176
EP - 181
DO - 10.5220/0007709301760181