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.
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Ghrab, A., Romero, O., Skhiri, S. et al. TopoGraph: an End-To-End Framework to Build and Analyze Graph Cubes. Inf Syst Front 23, 203–226 (2021). https://doi.org/10.1007/s10796-020-10000-z
- Graph cube
- OLAP cube
- Graph processing
- Graph mining
- Multidimensional graph