cgmOLAP: Efficient Parallel Generation and Querying of Terabyte Size ROLAP Data Cubes.

Ying Chen, Frank Dehne, Todd Eavis, Andrew Rau-Chaplin

Abstract: In this demo we present the cgmOLAP server, the first fully functional parallel OLAP system able to build data cubes at a rate of more than 1 Terabyte per hour. cgmOLAP incorporates a variety of novel approaches for the parallel computation of full cubes, partial cubes, and iceberg cubes as well as new parallel cube indexing schemes. The cgmOLAP system consists of an application interface, a parallel query engine, a parallel cube materialization engine, meta data and cost model repositories, and shared server components that provide uniform management of I/O, memory, communications, and disk resources. The cgmOLAP demo system will be running on two 32 processor Linux-based clusters, one located in Canada the other in Australia. Our demonstration interface consists of three parts: (1) a cube specification panel that allows attendees to initiate the parallel generation of full, partial, or iceberg cubes (2) a query specification panel that allows attendees to initiate parallel range, rollup, drilldown, slice, dice and pivot queries (3) a performance monitoring panel which supports the visualization of cube generation and querying performance parameters such as rows generated, or queries evaluated, per second.