@inproceedings{7f849acd5b7c4bc69c351467158c39bc,
title = "Data Compression Cost Optimization",
abstract = "This paper proposes a general optimization framework to allocate computing resources to the compression of massive and heterogeneous data sets incident upon a communication or storage system. The framework is formulated using abstract parameters, and builds on rigorous tools from optimization theory. The outcome is a set of algorithms that together can reach optimal compression allocation in a realistic scenario involving a multitude of content types and compression tools. This claim is demonstrated by running the optimization algorithms on publicly available data sets, and showing up to 25% size reduction, with equal compute-time budget using standard compression tools.",
keywords = "Compression, Optimization, Performance",
author = "Eyal Zohar and Yuval Cassuto",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 Data Compression Conference, DCC 2015 ; Conference date: 07-04-2015 Through 09-04-2015",
year = "2015",
month = jul,
day = "2",
doi = "10.1109/DCC.2015.18",
language = "الإنجليزيّة",
series = "Data Compression Conference Proceedings",
pages = "393--402",
editor = "Ali Bilgin and Marcellin, {Michael W.} and Joan Serra-Sagrista and Storer, {James A.}",
booktitle = "Proceedings - DCC 2015",
}