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This article addresses the query performance issue for Relational OLAP (ROLAP) datacubes. We present RCUBE, a distributed multidimensional 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 with RCUBE show that the ROLAP advantage of better scalability, in comparison to MOLAP, can be maintained while providing a fast and flexible index for OLAP queries.

This paper presents an improved parallel method for generating ROLAP data cubes on a shared-nothing multiprocessor based on a novel optimized data partitioning technique. Since no shared disk is required, our method can be used for highly scalable processor clusters consisting of standard PCs with local disks only, connected via a data switch. Experiments show that our improved parallel method provides optimal, linear, speedup for at least 32 processors. The approach taken, which uses a ROLAP representation of the data cube, is well suited for large data warehouses and high dimensional data, and supports the generation of both fully materialized and partially materialized data cubes.

This chapter addresses the query performance issue for Relational OLAP (ROLAP) data cubes. The authors present RCUBE, 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. Their solution is efficient for spatial searches in high dimensions and scalable in terms of data sizes, dimensions, and number of processors. Their 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. Experiments with RCUBE 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.

In this book chapter, the authors discuss some important communication issues to obtain a highly scalable computing system. They consider the CGM (Coarse-Grained Multicomputer) model, a realistic computing model to obtain scalable parallel algorithms. The communication cost is modeled by the number of communication rounds and the objective is to design algorithms that require the minimum number of communication rounds. They discuss some important issues and make considerations of practical importance, based on our previous experience in the design and implementation of parallel algorithms. The first issue is the amount of data transmitted in a communication round. For a practical implementation to be successful they should attempt to minimize this amount, even when it is already within the limit allowed by the CGM model. The second issue concerns the trade-off between the number of communication rounds which the CGM attempts to minimize and the overall communication time taken in the communication rounds. Sometimes a larger number of communication rounds may actually reduce the total amount of data transmitted in the communications rounds. These two issues have guided us to present efficient parallel algorithms for the string similarity problem, used as an illustration.

This paper presents an improved parallel method for generating ROLAP data cubes on a shared-nothing multiprocessor based on a novel optimized data partitioning technique. Since no shared disk is required, our method can be used for highly scalable processor clusters consisting of standard PCs with local disks only, connected via a data switch. Experiments show that our improved parallel method provides optimal, linear, speedup for at least 32 processors. The approach taken, which uses a ROLAP representation of the data cube, is well suited for large data warehouses and high dimensional data, and supports the generation of both fully materialized and partially materialized data cubes.