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DTSTART:19700308T020000
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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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