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DTSTAMP:20250626T234541Z
LOCATION:B302-B305
DTSTART;TZID=America/New_York:20241119T120000
DTEND;TZID=America/New_York:20241119T170000
UID:submissions.supercomputing.org_SC24_sess487_post221@linklings.com
SUMMARY:NetCDFaster: A Geospatial Cyberinfrastructure Enhancing Multi-Dime
 nsional Scientific Dataset Access and Visualization Through Machine Learni
 ng Optimization
DESCRIPTION:Zhenlei Song and Zhe Zhang (Texas A&M University) and Alan Sus
 sman (University of Maryland)\n\nThis project introduces an enhanced solut
 ion for accessing and processing NetCDF data, a widely used standard in ge
 osciences for storing multidimensional data. Existing tools often compromi
 se on performance or lack full workflow support. The proposed system integ
 rates machine learning, specifically a CatBoost classifier, with a modern 
 web application to improve the speed and accuracy of data querying and vis
 ualization. It provides a user-friendly interface for uploading NetCDF fil
 es and extracting metadata efficiently. Experimental results demonstrate a
  64% F1-score in selecting optimal parameters and up to 80% improvement in
  processing time, significantly aiding scientific analysis.\n\nRegistratio
 n Category: Tech Program Reg Pass, Exhibits Reg Pass\n\nSession Chairs: Ay
 esha Afzal (Friedrich-Alexander University, Erlangen-Nuremberg; Erlangen N
 ational High Performance Computing Center); Sally Ellingson (University of
  Kentucky); and Alan Sussman (University of Maryland)\n\n
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