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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20211207T055410Z
LOCATION:Online
DTSTART;TZID=America/Chicago:20211114T170000
DTEND;TZID=America/Chicago:20211114T173000
UID:submissions.supercomputing.org_SC21_sess424_ws_drbsd107@linklings.com
SUMMARY:Exploring Lossy Compressibility through Statistical Correlations o
f Scientific Datasets
DESCRIPTION:Workshop\n\nExploring Lossy Compressibility through Statistica
l Correlations of Scientific Datasets\n\nKrasowska, Bessac, Underwood, Cal
houn, Cappello...\n\nLossy compression plays a growing role in scientific
simulations where the cost of storing their output data can span terabytes
. Using error bounded lossy compression reduces the amount of storage for
each simulation; however, there is no known bound for the upper limit on l
ossy compressibility. Correlation structures in the data, choice of compre
ssor and error bound are factors allowing larger compression ratios and im
proved quality metrics. Analyzing these three factors provides one directi
on towards quantifying lossy compressibility. As a first step, we explore
statistical methods to characterize the correlation structures present in
the data and their relationships, through functional models, to compressi
on ratios. We observed a relationship between compression ratios and stati
stics summarizing correlation structure of the data, which are a first ste
p towards evaluating the theoretical limits of lossy compressibility used
to eventually predict compression performance and adapt compressors to cor
relation structures present in the data.\n\nTag: Online Only, Applications
, Big Data, Data Analytics, Data Management\n\nRegistration Category: Work
shop Reg Pass
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