Online analytical processsing on graph data

"Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Da...

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Autores principales: Gómez, Leticia Irene, Kuijpers, Bart, Vaisman, Alejandro Ariel
Formato: Artículos de Publicaciones Periódicas publishedVersion
Lenguaje:Inglés
Publicado: 2020
Materias:
OLAP
ALMACENES DE DATOS
ANALISIS DE DATOS
Acceso en línea:http://ri.itba.edu.ar/handle/123456789/3260
Aporte de:
Repositorio Institucional Instituto Tecnológico de Buenos Aires (ITBA) de Instituto Tecnológico de Buenos Aires (ITBA)
id I32-R138-123456789-3260
record_format dspace
spelling I32-R138-123456789-32602022-12-07T13:05:52Z Online analytical processsing on graph data Gómez, Leticia Irene Kuijpers, Bart Vaisman, Alejandro Ariel OLAP ALMACENES DE DATOS ANALISIS DE DATOS "Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Data” scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multihypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive." 2020-12-17T15:37:14Z 2020-12-17T15:37:14Z 2020 Artículos de Publicaciones Periódicas info:eu-repo/semantics/publishedVersion http://ri.itba.edu.ar/handle/123456789/3260 en info:eu-repo/semantics/reference/doi/10.3233/IDA-194576 info:eu-repo/grantAgreement/UH/BOF/16KV09/BE. Hasselt info:eu-repo/grantAgreement/ANPCyT/PICT/2014-0787/AR. Ciudad Autónoma de Buenos Aires info:eu-repo/grantAgreement/ANPCyT/PICT/2017-1054/AR. Ciudad Autónoma de Buenos Aires 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 OLAP
ALMACENES DE DATOS
ANALISIS DE DATOS
spellingShingle OLAP
ALMACENES DE DATOS
ANALISIS DE DATOS
Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
Online analytical processsing on graph data
topic_facet OLAP
ALMACENES DE DATOS
ANALISIS DE DATOS
description "Online Analytical Processing (OLAP) comprises tools and algorithms that allow querying multidimensional databases. It is based on the multidimensional model, where data can be seen as a cube such that each cell contains one or more measures that can be aggregated along dimensions. In a “Big Data” scenario, traditional data warehousing and OLAP operations are clearly not sufficient to address current data analysis requirements, for example, social network analysis. Furthermore, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. Nevertheless, there is not much work on the problem of taking OLAP analysis to the graph data model. This paper proposes a formal multidimensional model for graph analysis, that considers the basic graph data, and also background information in the form of dimension hierarchies. The graphs in this model are node- and edge-labelled directed multihypergraphs, called graphoids, which can be defined at several different levels of granularity using the dimensions associated with them. Operations analogous to the ones used in typical OLAP over cubes are defined over graphoids. The paper presents a formal definition of the graphoid model for OLAP, proves that the typical OLAP operations on cubes can be expressed over the graphoid model, and shows that the classic data cube model is a particular case of the graphoid data model. Finally, a case study supports the claim that, for many kinds of OLAP-like analysis on graphs, the graphoid model works better than the typical relational OLAP alternative, and for the classic OLAP queries, it remains competitive."
format Artículos de Publicaciones Periódicas
publishedVersion
author Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
author_facet Gómez, Leticia Irene
Kuijpers, Bart
Vaisman, Alejandro Ariel
author_sort Gómez, Leticia Irene
title Online analytical processsing on graph data
title_short Online analytical processsing on graph data
title_full Online analytical processsing on graph data
title_fullStr Online analytical processsing on graph data
title_full_unstemmed Online analytical processsing on graph data
title_sort online analytical processsing on graph data
publishDate 2020
url http://ri.itba.edu.ar/handle/123456789/3260
work_keys_str_mv AT gomezleticiairene onlineanalyticalprocesssingongraphdata
AT kuijpersbart onlineanalyticalprocesssingongraphdata
AT vaismanalejandroariel onlineanalyticalprocesssingongraphdata
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