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Qi Zhang

Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Standard

Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. / Vestergaard, Rasmus; Lucani Rötter, Daniel Enrique; Zhang, Qi.
European Wireless Conference. VDE Verlag GmbH, 2019. s. 67-71.

Publikation: Bidrag til bog/antologi/rapport/proceeding › Konferencebidrag i proceedings › Forskning › peer review

Harvard

Vestergaard, R, Lucani Rötter, DE & Zhang, Q 2019, Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. i European Wireless Conference. VDE Verlag GmbH, s. 67-71, European Wireless 2019 - 25th European Wireless Conference, Aarhus, Danmark, 02/05/2019. <https://ieeexplore.ieee.org/document/8835941>

APA

Vestergaard, R., Lucani Rötter, D. E., & Zhang, Q. (2019). Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. I European Wireless Conference (s. 67-71). VDE Verlag GmbH. https://ieeexplore.ieee.org/document/8835941

CBE

Vestergaard R, Lucani Rötter DE, Zhang Q. 2019. Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. I European Wireless Conference. VDE Verlag GmbH. s. 67-71.

MLA

Vestergaard, Rasmus, Daniel Enrique Lucani Rötter, og Qi Zhang "Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data". European Wireless Conference. VDE Verlag GmbH. 2019, 67-71.

Vancouver

Vestergaard R, Lucani Rötter DE, Zhang Q. Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data. I European Wireless Conference. VDE Verlag GmbH. 2019. s. 67-71

Author

Vestergaard, Rasmus ; Lucani Rötter, Daniel Enrique ; Zhang, Qi. / Generalized Deduplication : Lossless Compression for Large Amounts of Small IoT Data. European Wireless Conference. VDE Verlag GmbH, 2019. s. 67-71

Bibtex

@inproceedings{1974e0216f3343cd90868e0a200793b6,
title = "Generalized Deduplication: Lossless Compression for Large Amounts of Small IoT Data",
abstract = "We show that a generalization of deduplication can enable compressed storage of sensor data. The method uses error- correcting codes in a non-traditional manner to identify similar elements, and then leverages this similarity for compression. Using Reed Solomon codes, our method has a theoretical potential to reduce the cost of storing chunks of 16 bytes to as much as 5 times less, and up to 65 times less for chunks of 255 bytes. We define a simple model for sensor data, and show how our approach is able to compress data from the model, realizing its compression potential with much smaller data sets than classic deduplication requires. This demonstrates that generalized deduplication can be a viable solution for practical lossless compression of small IoT data in scenarios where classic deduplication is ineffective.",
author = "Rasmus Vestergaard and {Lucani R{\"o}tter}, {Daniel Enrique} and Qi Zhang",
year = "2019",
language = "English",
isbn = "978-3-8007-4948-5",
pages = "67--71",
booktitle = "European Wireless Conference",
publisher = "VDE Verlag GmbH",
note = "European Wireless 2019 - 25th European Wireless Conference ; Conference date: 02-05-2019 Through 05-05-2019",
url = "https://www.vde-verlag.de/proceedings-en/564948016.html",

}

RIS

TY - GEN

T1 - Generalized Deduplication

T2 - European Wireless 2019 - 25th European Wireless Conference

AU - Vestergaard, Rasmus

AU - Lucani Rötter, Daniel Enrique

AU - Zhang, Qi

PY - 2019

Y1 - 2019

N2 - We show that a generalization of deduplication can enable compressed storage of sensor data. The method uses error- correcting codes in a non-traditional manner to identify similar elements, and then leverages this similarity for compression. Using Reed Solomon codes, our method has a theoretical potential to reduce the cost of storing chunks of 16 bytes to as much as 5 times less, and up to 65 times less for chunks of 255 bytes. We define a simple model for sensor data, and show how our approach is able to compress data from the model, realizing its compression potential with much smaller data sets than classic deduplication requires. This demonstrates that generalized deduplication can be a viable solution for practical lossless compression of small IoT data in scenarios where classic deduplication is ineffective.

AB - We show that a generalization of deduplication can enable compressed storage of sensor data. The method uses error- correcting codes in a non-traditional manner to identify similar elements, and then leverages this similarity for compression. Using Reed Solomon codes, our method has a theoretical potential to reduce the cost of storing chunks of 16 bytes to as much as 5 times less, and up to 65 times less for chunks of 255 bytes. We define a simple model for sensor data, and show how our approach is able to compress data from the model, realizing its compression potential with much smaller data sets than classic deduplication requires. This demonstrates that generalized deduplication can be a viable solution for practical lossless compression of small IoT data in scenarios where classic deduplication is ineffective.

M3 - Article in proceedings

SN - 978-3-8007-4948-5

SP - 67

EP - 71

BT - European Wireless Conference

PB - VDE Verlag GmbH

Y2 - 2 May 2019 through 5 May 2019

ER -