Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs.
Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach.
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro Vaisman, and Esteban Zimany, A Framework for Builidng OLAP Cubes on Graphs, proceedings of the 19th East-European Conference on Advances in Databases and Information Systems, Poitiers, France, September 2015.