Performing OLAP over Graph Data: Query Language, Implementation, and a Case Study

Abstract

In current “Big Data” scenarios, traditional data warehousing and Online Analytical Processing (OLAP) operations on cubes are clearly not sufficient to address the current data analysis requirements. Nevertheless, OLAP operations and models can expand the possibilities of graph analysis beyond the traditional graph-based computation. In spite of this, there is not much work on the problem of taking OLAP analysis to the graph data model. In previous work we proposed a multidimensional (MD) data model for graph analysis, that considers not only the basic graph data, but background information in the form of dimension hierarchies as well. The graphs in our model are node- and edgelabelled directed multi-hypergraphs, called graphoids, defined at several different levels of granularity. In this paper we show how we implemented this proposal over Neo4J, the most popular graph database nowadays, discuss implementation issues, and present a detailed case study to show how OLAP operations can be used on graphs.