Parallel Multi-Dimensional ROLAP Indexing.

Frank Dehne, Todd Eavis, and Andrew Rau-Chaplin

Abstract:  This paper addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present a distributed multi-dimensional ROLAP indexing scheme which is practical to implement, requires only a small communication volume, and is fully adapted to distributed disks. Our solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Our method is also incrementally maintainable. Using "surrogate" group-bys, it allows for the efficient processing of arbitrary OLAP queries on partial cubes, where not all of the group-bys have been materialized.

Our experiments show that the ROLAP advantage of better scalability, in comparison to MOLAP, can be maintained while providing, at the same time, a fast and flexible index for OLAP queries.

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