| Document Date: 2014-06-07 16:49:22
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City Sampling / Athens / Analysis / San Diego / / Company IBM / Large Storage Systems / Mass Storage Systems / NetApp / Oracle Corporation / Database Systems / Intel / / Country Greece / / Currency USD / / / Event Product Recall / Product Issues / / IndustryTerm compression algorithm / prominent tool / large file systems / distributed algorithm / post processing step / file systems / petabyte-scale file systems / implement solutions / basic sampling algorithm / compression estimation tool / distributed traversal with sampling tool / large systems / pre-processing step / parallel file systems / Student solutions / search trees / large-scale storage systems / streaming algorithms / translation algorithm / prominent compression product / video processing / large scale file systems / metadata search / pre-processing / learning algorithms / / OperatingSystem Solaris ZFS / / Organization American Statistical Association / USENIX Association / / Person S. Seshadri / Lynne Stokes / Nicholas Murphy / Jeffrey Scott Vitter / Satyajayant Misra / Dmitry Sotnikov / Jharrod LaFon / Brent Welch / Jeffrey F. Naughton / Peter J. Haas / Geoffrey Noer / Jon Bringhurst / / Position enterprise mid range storage controller / representative / / Product Impressions tools / calls / Impressions / / ProgrammingLanguage C / / ProvinceOrState Manitoba / California / / PublishedMedium Journal of the American Statistical Association / / Technology one-pass algorithm / RAM / jpeg / underlying algorithms / text algorithms / translation algorithm / random access / Operating Systems / specific compression algorithm / speech algorithm / basic sampling algorithm / text algorithm / /
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The Case for Sampling on Very Large File Systems George Goldberg, Danny Harnik, Dmitry Sotnikov IBM Research–Haifa, {georgeg, dannyh,dmitrys}@il.ibm.com Abstract—Sampling has long been a prominent tool in statistics