TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes

Amine Ghrab (), Oscar Romero (), Sabri Skhiri () and Esteban Zimányi ()
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Amine Ghrab: EURA NOVA
Oscar Romero: Universitat Politècnica de Catalunya
Sabri Skhiri: EURA NOVA
Esteban Zimányi: Université Libre de Bruxelles

Information Systems Frontiers, 2021, vol. 23, issue 1, No 13, 203-226

Abstract: Abstract Graphs are a fundamental structure that provides an intuitive abstraction for modeling and analyzing complex and highly interconnected data. Given the potential complexity of such data, some approaches proposed extending decision-support systems with multidimensional analysis capabilities over graphs. In this paper, we introduce TopoGraph, an end-to-end framwork for building and analyzing graph cubes. TopoGraph extends the existing graph cube models by defining new types of dimensions and measures and organizing them within a multidimensional space that guarantees multidimensional integrity constraints. This results in defining three new types of graph cubes: property graph cubes, topological graph cubes, and graph-structured cubes. Afterwards, we define the algebraic OLAP operations for such novel cubes. We implement and experimentally validate TopoGraph with different types of real-world datasets.

Keywords: Graph cube; OLAP cube; Graph processing; Graph mining; Multidimensional graph (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s10796-020-10000-z

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