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DTSTAMP:20181221T160743Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181114T083000
DTEND;TZID=America/Chicago:20181114T170000
UID:submissions.supercomputing.org_SC18_sess342_drs101@linklings.com
SUMMARY:Pattern Matching on Massive Metadata Graphs at Scale
DESCRIPTION:Doctoral Showcase\nWorkshop Reg Pass, Tutorial Reg Pass, Tech 
 Program Reg Pass, Exhibits Reg Pass, Exhibits - Exhibit Hall Only Reg Pass
 \n\nPattern Matching on Massive Metadata Graphs at Scale\n\nReza, Ripeanu\
 n\nPattern matching is a powerful graph analysis tool. Unfortunately, exis
 ting solutions have limited scalability, support only a limited set of pat
 terns, and/or focus on only a subset of the real-world problems associated
  with pattern matching. First, we present a new algorithmic pipeline based
  on graph pruning that: (i) enables highly scalable exact pattern matching
  on labeled graphs, (ii) supports arbitrary patterns, (iii) enables trade-
 offs between precision and time-to-solution, and (iv) supports a set of po
 pular analytics scenarios. We implement our approach on top of HavoqGT and
  demonstrate its advantages through strong and weak scaling experiments on
  massive-scale real-world (up to 257B edges) and synthetic (up to 4.4T edg
 es) graphs, respectively, and at scales (1,024 nodes / 36,864 cores) order
 s of magnitude larger than used in the past for similar problems. Furtherm
 ore, we explore avenues to enable approximate matching within the graph pr
 uning model, targeting contemporary and emerging high-impact, real-world a
 pplications.
URL:https://sc18.supercomputing.org/presentation/?id=drs101&sess=sess342
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