The cgmCUBE project: Optimizing parallel data cube generation for ROLAP
Distributed and Parallel Databases
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On-line Analytical Processing (OLAP) has become a fundamental component of contemporary decision support systems and represents a means by which knowledge workers can efficiently analyze vast amounts of organizational data. Within the OLAP context, one of the more interesting recent themes has been the computation and manipulation of the data cube, a relational model that can be used to represent summarized multi-dimensional views of massive data warehousing archives. Over the past five or six years a number of efficient sequential algorithms for data cube construction have been presented. Given the size of the underlying data sets, however, it is perhaps surprising that relatively little effort has been expended on the design of load balanced, communication efficient algorithms for the parallelization of the data cube. This thesis investigates such opportunities, with a particular emphasis upon coarse-grained, distributed memory parallel architectures. New parallel algorithms for the computation of both the complete data cube and the partial data cube are presented. In addition, a model for distributed multi-dimensional indexing is proposed. The associated parallel query engine not only supports efficient range queries, but query resolution on non-materialized views and views containing hierarchical attributes. All of the proposed algorithms and data structures have been fully implemented and evaluated on contemporary distributed memory parallel machines.