Modelling and querying star and snowflake warehouses using graph databases

"In current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graphbased computation. This paper studies graph databases as an alternative to implement star and...

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Detalles Bibliográficos
Autores principales: Vaisman, Alejandro Ariel, Besteiro, María Florencia, Valverde Melito, Maximiliano Javier
Formato: Ponencias en Congresos acceptedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
ALMACENES DE DATOS
BASES DE DATOS ORIENTADAS A GRAFOS
SISTEMAS DE INFORMACION
ANALISIS DE DATOS
OLAP
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/2231
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-2231
record_format dspace
spelling I32-R138-123456789-22312022-12-07T14:13:47Z Modelling and querying star and snowflake warehouses using graph databases Vaisman, Alejandro Ariel Besteiro, María Florencia Valverde Melito, Maximiliano Javier ALMACENES DE DATOS BASES DE DATOS ORIENTADAS A GRAFOS SISTEMAS DE INFORMACION ANALISIS DE DATOS OLAP "In current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graphbased computation. This paper studies graph databases as an alternative to implement star and snowflake schemas, the typical choices for data warehouse design. For this, the MusicBrainz database is used. A data warehouse for this database is designed, and implemented over a Postgres relational database. This warehouse is also represented as a graph, and implemented over the Neo4j graph database. A collection of typical OLAP queries is used to compare both implementations. The results reported here show that in ten out of thirteen queries tested, the graph implementation outperforms the relational one, in ratios that go from 1.3 to 26 times faster, and performs similarly to the relational implementation in the three remaining cases." 2020-06-26T20:51:31Z 2020-06-26T20:51:31Z 2019 Ponencias en Congresos info:eu-repo/semantics/acceptedVersion 978-3030-302-77-1 1865-0929 http://ri.itba.edu.ar/handle/123456789/2231 en info:eu-repo/grantAgreement/ANPCyT/PICT/2017-1054/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-030-30278-8_18 application/pdf
institution Instituto Tecnológico de Buenos Aires (ITBA)
institution_str I-32
repository_str R-138
collection Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA)
language Inglés
topic ALMACENES DE DATOS
BASES DE DATOS ORIENTADAS A GRAFOS
SISTEMAS DE INFORMACION
ANALISIS DE DATOS
OLAP
spellingShingle ALMACENES DE DATOS
BASES DE DATOS ORIENTADAS A GRAFOS
SISTEMAS DE INFORMACION
ANALISIS DE DATOS
OLAP
Vaisman, Alejandro Ariel
Besteiro, María Florencia
Valverde Melito, Maximiliano Javier
Modelling and querying star and snowflake warehouses using graph databases
topic_facet ALMACENES DE DATOS
BASES DE DATOS ORIENTADAS A GRAFOS
SISTEMAS DE INFORMACION
ANALISIS DE DATOS
OLAP
description "In current “Big Data” scenarios, graph databases are increasingly being used. Online Analytical Processing (OLAP) operations can expand the possibilities of graph analysis beyond the traditional graphbased computation. This paper studies graph databases as an alternative to implement star and snowflake schemas, the typical choices for data warehouse design. For this, the MusicBrainz database is used. A data warehouse for this database is designed, and implemented over a Postgres relational database. This warehouse is also represented as a graph, and implemented over the Neo4j graph database. A collection of typical OLAP queries is used to compare both implementations. The results reported here show that in ten out of thirteen queries tested, the graph implementation outperforms the relational one, in ratios that go from 1.3 to 26 times faster, and performs similarly to the relational implementation in the three remaining cases."
format Ponencias en Congresos
acceptedVersion
author Vaisman, Alejandro Ariel
Besteiro, María Florencia
Valverde Melito, Maximiliano Javier
author_facet Vaisman, Alejandro Ariel
Besteiro, María Florencia
Valverde Melito, Maximiliano Javier
author_sort Vaisman, Alejandro Ariel
title Modelling and querying star and snowflake warehouses using graph databases
title_short Modelling and querying star and snowflake warehouses using graph databases
title_full Modelling and querying star and snowflake warehouses using graph databases
title_fullStr Modelling and querying star and snowflake warehouses using graph databases
title_full_unstemmed Modelling and querying star and snowflake warehouses using graph databases
title_sort modelling and querying star and snowflake warehouses using graph databases
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/2231
work_keys_str_mv AT vaismanalejandroariel modellingandqueryingstarandsnowflakewarehousesusinggraphdatabases
AT besteiromariaflorencia modellingandqueryingstarandsnowflakewarehousesusinggraphdatabases
AT valverdemelitomaximilianojavier modellingandqueryingstarandsnowflakewarehousesusinggraphdatabases
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