Change a Bit to save Bytes:: Compression for Floating Point Time-Series Data

Francesco Taurone; Daniel Enrique Lucani Rötter; Marcell Fehér; Qi Zhang

doi:10.1109/ICC45041.2023.10279204

Change a Bit to save Bytes: Compression for Floating Point Time-Series Data

Francesco Taurone
, Daniel Enrique Lucani Rötter
, Marcell Fehér
, Qi Zhang

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

4 Citations (Scopus)

15 Downloads (Pure)

Abstract

The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80% when allowing for at most 1% of recovery error.

Original language	English
Title of host publication	ICC 2023 - IEEE International Conference on Communications : Sustainable Communications for Renaissance
Editors	Michele Zorzi, Meixia Tao, Walid Saad
Number of pages	6
Publisher	IEEE
Publication date	Oct 2023
Pages	3756-3761
ISBN (Print)	978-1-5386-7463-5
ISBN (Electronic)	978-1-5386-7462-8
DOIs	https://doi.org/10.1109/ICC45041.2023.10279204
Publication status	Published - Oct 2023

Keywords

IoT
data compression
floating point
generalized deduplication
preprocessing

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10.1109/ICC45041.2023.10279204

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Cite this

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title = "Change a Bit to save Bytes:: Compression for Floating Point Time-Series Data",

abstract = "The number of IoT devices is expected to continue its dramatic growth in the coming years and, with it, a growth in the amount of data to be transmitted, processed and stored. Compression techniques that support analytics directly on the compressed data could pave the way for systems to scale efficiently to these growing demands. This paper proposes two novel methods for preprocessing a stream of floating point data to improve the compression capabilities of various IoT data compressors. In particular, these techniques are shown to be helpful with recent compressors that allow for random access and analytics while maintaining good compression. Our techniques improve compression with reductions up to 80\% when allowing for at most 1\% of recovery error. ",

keywords = "IoT, data compression, floating point, generalized deduplication, preprocessing",

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Change a Bit to save Bytes: Compression for Floating Point Time-Series Data. / Taurone, Francesco; Lucani Rötter, Daniel Enrique; Fehér, Marcell et al.
ICC 2023 - IEEE International Conference on Communications: Sustainable Communications for Renaissance. ed. / Michele Zorzi; Meixia Tao; Walid Saad. IEEE, 2023. p. 3756-3761.

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

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