Studies of Big Data metadata segmentation between relational and non-relational databases

In recent years the concepts of Big Data became well established in IT. Systems managing large data volumes produce metadata that describe data and workflows. These metadata are used to obtain information about current system state and for statistical and trend analysis of the processes these system...

Descripción completa

Detalles Bibliográficos
Autores principales: Golosova, M V, Grigorieva, M A, Klimentov, A A, Ryabinkin, E A, Dimitrov, G, Potekhin, M
Lenguaje:eng
Publicado: 2015
Materias:
Computing and Computers
Acceso en línea:https://dx.doi.org/10.1088/1742-6596/664/4/042023
http://cds.cern.ch/record/2134566
_version_ 1780949905984978944
author Golosova, M V
Grigorieva, M A
Klimentov, A A
Ryabinkin, E A
Dimitrov, G
Potekhin, M
author_facet Golosova, M V
Grigorieva, M A
Klimentov, A A
Ryabinkin, E A
Dimitrov, G
Potekhin, M
author_sort Golosova, M V
collection CERN
description In recent years the concepts of Big Data became well established in IT. Systems managing large data volumes produce metadata that describe data and workflows. These metadata are used to obtain information about current system state and for statistical and trend analysis of the processes these systems drive. Over the time the amount of the stored metadata can grow dramatically. In this article we present our studies to demonstrate how metadata storage scalability and performance can be improved by using hybrid RDBMS/NoSQL architecture.
id oai-inspirehep.net-1413855
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2015
record_format invenio
spelling oai-inspirehep.net-14138552022-08-10T13:00:53Zdoi:10.1088/1742-6596/664/4/042023http://cds.cern.ch/record/2134566engGolosova, M VGrigorieva, M AKlimentov, A ARyabinkin, E ADimitrov, GPotekhin, MStudies of Big Data metadata segmentation between relational and non-relational databasesComputing and ComputersIn recent years the concepts of Big Data became well established in IT. Systems managing large data volumes produce metadata that describe data and workflows. These metadata are used to obtain information about current system state and for statistical and trend analysis of the processes these systems drive. Over the time the amount of the stored metadata can grow dramatically. In this article we present our studies to demonstrate how metadata storage scalability and performance can be improved by using hybrid RDBMS/NoSQL architecture.oai:inspirehep.net:14138552015
spellingShingle Computing and Computers
Golosova, M V
Grigorieva, M A
Klimentov, A A
Ryabinkin, E A
Dimitrov, G
Potekhin, M
Studies of Big Data metadata segmentation between relational and non-relational databases
title Studies of Big Data metadata segmentation between relational and non-relational databases
title_full Studies of Big Data metadata segmentation between relational and non-relational databases
title_fullStr Studies of Big Data metadata segmentation between relational and non-relational databases
title_full_unstemmed Studies of Big Data metadata segmentation between relational and non-relational databases
title_short Studies of Big Data metadata segmentation between relational and non-relational databases
title_sort studies of big data metadata segmentation between relational and non-relational databases
topic Computing and Computers
url https://dx.doi.org/10.1088/1742-6596/664/4/042023
http://cds.cern.ch/record/2134566
work_keys_str_mv AT golosovamv studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases
AT grigorievama studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases
AT klimentovaa studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases
AT ryabinkinea studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases
AT dimitrovg studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases
AT potekhinm studiesofbigdatametadatasegmentationbetweenrelationalandnonrelationaldatabases

Ejemplares similares