A Graph-oriented Framework for Online Analytical Processing

Abstract

OLAP (Online Analytical Processing) is a tried-and-tested technology and a core concept in Business Intelligence. With data flowing from different and countless sources, exploring data in order to deliver actionable insights has become a daunting task with current OLAP tools despite the cycle of improvement that has gone through it. In the last decade, with the emergence of the big data phenomenon, NoSQL databases are seeing a spike in popularity and become more used in industry and academia as their value in handling a huge and varied amount of data become increasingly evident. Graph oriented database is one of the four chief types of NoSQL oriented databases that represent a promising technology candidate for big data analytics. In this paper we bring forward our contribution to graph-oriented analytical processing, which is twofold. First, we provide a novel approach for modeling a graph-oriented data warehouse. Second, we propose a data cube materialization through the precomputation of aggregated nodes. We present how typical OLAP queries can be performed against data warehouses stored in NoSQL graph-oriented database management systems. An implementation is conducted on a fictional data warehouse using Neo4j and the Cypher declarative language. The same dataset is stored in a relational data warehouse in order to compare storage space and query performance. Thus, the obtained results shows that graph OLAP implementation outperform clearly the relational alternative in term of query response time.