research-article

Online analytical processsing on graph data

Authors: Leticia Gómez, Bart Kuijpers, Alejandro VaismanAuthors Info & Claims

Intelligent Data Analysis, Volume 24, Issue 3

Pages 515 - 541

https://doi.org/10.3233/IDA-194576

Published: 01 January 2020 Publication History

Abstract

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 multi-hypergraphs, 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.

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Cited By

Bimonte SGallinucci EMarcel PRizzi S(2023)Logical design of multi-model data warehousesKnowledge and Information Systems10.1007/s10115-022-01788-065:3(1067-1103)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10115-022-01788-0
Rizzi S(2022)OLAP and NoSQL: Happily Ever AfterAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_4(35-44)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_4

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Online analytical processsing on graph data

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cover image Intelligent Data Analysis

Intelligent Data Analysis Volume 24, Issue 3

2020

240 pages

ISSN:1088-467X

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© 2020 – IOS Press and the authors. All rights reserved.

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IOS Press

Netherlands

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Published: 01 January 2020

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Cited By

Bimonte SGallinucci EMarcel PRizzi S(2023)Logical design of multi-model data warehousesKnowledge and Information Systems10.1007/s10115-022-01788-065:3(1067-1103)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s10115-022-01788-0
Rizzi S(2022)OLAP and NoSQL: Happily Ever AfterAdvances in Databases and Information Systems10.1007/978-3-031-15740-0_4(35-44)Online publication date: 5-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-15740-0_4

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