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Gaining insight from large data volumes with ease
Efficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives (profits, cost-reduction, various optimization of data...
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Lenguaje: | eng |
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2018
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Acceso en línea: | http://cds.cern.ch/record/2647100 |
_version_ | 1780960535932567552 |
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author | Kuznetsov, Valentin |
author_facet | Kuznetsov, Valentin |
author_sort | Kuznetsov, Valentin |
collection | CERN |
description | Efficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives (profits, cost-reduction, various optimization of data workflows, and pipelines). In this paper, we discuss how modern technologies are transforming well established patterns in HEP communities. The new data insight can be achieved by embracing Big Data tools for a variety of use-cases, from analytics and monitoring to training Machine Learning models on a terabyte scale. We provide concrete examples within context of the CMS experiment where Big Data tools are already playing or would play a significant role in daily operations. |
id | cern-2647100 |
institution | Organización Europea para la Investigación Nuclear |
language | eng |
publishDate | 2018 |
record_format | invenio |
spelling | cern-26471002019-09-30T06:29:59Zhttp://cds.cern.ch/record/2647100engKuznetsov, ValentinGaining insight from large data volumes with easeDetectors and Experimental TechniquesEfficient handling of large data-volumes becomes a necessity in today's world. It is driven by the desire to get more insight from the data and to gain a better understanding of user trends which can be transformed into economic incentives (profits, cost-reduction, various optimization of data workflows, and pipelines). In this paper, we discuss how modern technologies are transforming well established patterns in HEP communities. The new data insight can be achieved by embracing Big Data tools for a variety of use-cases, from analytics and monitoring to training Machine Learning models on a terabyte scale. We provide concrete examples within context of the CMS experiment where Big Data tools are already playing or would play a significant role in daily operations.CMS-CR-2018-209oai:cds.cern.ch:26471002018-09-18 |
spellingShingle | Detectors and Experimental Techniques Kuznetsov, Valentin Gaining insight from large data volumes with ease |
title | Gaining insight from large data volumes with ease |
title_full | Gaining insight from large data volumes with ease |
title_fullStr | Gaining insight from large data volumes with ease |
title_full_unstemmed | Gaining insight from large data volumes with ease |
title_short | Gaining insight from large data volumes with ease |
title_sort | gaining insight from large data volumes with ease |
topic | Detectors and Experimental Techniques |
url | http://cds.cern.ch/record/2647100 |
work_keys_str_mv | AT kuznetsovvalentin gaininginsightfromlargedatavolumeswithease |