Online Analytical Processsing on Graph Data

TL;DR

This paper introduces a formal multidimensional graph model called graphoids for OLAP analysis, extending traditional cube operations to graph data, and demonstrates its effectiveness through a case study.

Contribution

It proposes a novel formal graph-based multidimensional model for OLAP, called graphoids, that generalizes traditional data cubes and supports complex graph analysis.

Findings

Graphoid model can express typical OLAP operations on graphs.

The model generalizes the classic data cube, encompassing it as a special case.

Case study shows graphoids outperform relational OLAP in graph analysis tasks.

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.