Fusion OLAP: Fusing the Pros of MOLAP and ROLAP Together for In-memory OLAP (Extended Abstract)

OLAP models can be categorized with two types: MOLAP (multidimensional OLAP) and ROLAP (relational OLAP). In particular, MOLAP is efficient in multidimensional computing at the cost of cube maintenance, while ROLAP reduces the data storage size at the cost of expensive multidimensional join operations. In this paper, we propose a novel Fusion OLAP model to fuse the multi-dimensional computing model and relational storage model together to make the best aspects of both MOLAP and ROLAP worlds. The Fusion OLAP model can be integrated into the state-of-the-art in-memory databases with additional surrogate key indexes and vector indexes. We compared the Fusion OLAP implementations with three leading analytical in-memory databases. Our comprehensive experimental results show that Fusion OLAP implementation can achieve up to 35%, 365% and 169% performance improvements based on the Hyper, Vectorwise and MonetDB databases respectively, for the Star Schema Benchmark (SSB) with scale factor 100.