Graphs are a fundamental structure that provides an intuitive abstraction for modelling 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 framework 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.

 

The paper will be published soon in Information Systems Frontiers, and is already available online on Springer. Currently, it is unfortunately available only to subscribers, but do not hesitate to reach out to us for more information!

 

Amine Ghrab, Oscar Romero, Sabri Skhiri, Esteban Zimányi, TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes, published in Information Systems Frontiers (2020).