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DTSTAMP:20190719T085743Z
LOCATION:HG F 3
DTSTART;TZID=Europe/Stockholm:20190612T143000
DTEND;TZID=Europe/Stockholm:20190612T150000
UID:submissions.pasc-conference.org_PASC19_sess117_msa163@linklings.com
SUMMARY:Distributed Data Analysis in High Energy Physics with Spark
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Physi
 cs, Life Sciences\n\nDistributed Data Analysis in High Energy Physics with
  Spark\n\nCervantes Villanueva, Tejedor Saavedra\n\nAfter a remarkable era
  of great discoveries, particle physics has an ambitious and broad experim
 ental program aiming to expand the limits of our knowledge about our unive
 rse. The roadmap for the coming decades is full of big challenges with the
  Large Hadron Collider (LHC) at the forefront of scientific research. The 
 High Energy Physics (HEP) community has developed specialized solutions fo
 r processing LHC experiment data over decades. However, in the 2020s an up
 graded version of the LHC, High-Luminosity LHC (HL-LHC), is expected to pr
 oduce some 30 times more data than the LHC has currently produced. As the 
 total amount of LHC data already collected is close to an exabyte, the for
 eseen evolution in hardware technology will not be enough to face this new
  data processing challenge and software will have to cover the gap. Theref
 ore, there is a strong need for tools to easily express physics analyses i
 n a high-level way, as well as to automatically parallelize those analyses
  on new parallel and distributed infrastructures. In light of this, ROOT, 
 a framework for data-analysis in HEP, is developing new tools for declarat
 ive analysis able to integrate with BigData technologies from industry and
  to reduce the “time to insight” for physicists.
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