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DTSTAMP:20211207T055358Z
LOCATION:Second Floor Atrium
DTSTART;TZID=America/Chicago:20211117T083000
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UID:submissions.supercomputing.org_SC21_sess279_rpost123@linklings.com
SUMMARY:Capturing Relationships Based on Structure Similarity for Self-Des
 cribing Scientific Data Formats
DESCRIPTION:Posters, Research Posters\n\nCapturing Relationships Based on 
 Structure Similarity for Self-Describing Scientific Data Formats\n\nNiu, Z
 hang, Byna, Chen\n\nMany scientific data sets use self-describing files fo
 r storing data, and files within a data set are often isolated without any
  definition of relationships among them. Because of the isolated managemen
 t of scientific data files, locating, assimilating and utilizing relations
 hips for a given query remains a long-standing problem in data discovery. 
 Many relationships are often hidden in complex file structures where diffe
 rent kinds of relationships are not explicitly categorized. To build relat
 ionships among scientific data files that are stored in self-describing fo
 rmats, we propose a relationship-capturing method based on structure simil
 arity determination of the files. Similarity is a fundamental concept in c
 omputer science. Crucially, "similarity" is not a typical relationship tha
 t reflects correlations of properties in different scientific files. We ca
 n use structure similarity determination to capture important relationship
 s. We have evaluated our approach on real-world scientific files. Our appr
 oach demonstrates effective relationship capturing and efficient relations
 hip building performance.\n\nRegistration Category: Tech Program Reg Pass,
  Exhibit Hall Only
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