ParaCube: A Scalable OLAP Model Based on Distributed Aggregate Computing with Sibling Cubes

The requirements of OLAP applications increase rapidly by dramatically increased data volume, users, query volume and query complexity. The requirement for shortening update period in data warehouse is another crucial factor for a scalable OLAP application. In this paper, we propose a scalable OLAP prototype to support the query processing with increasing data volume by distributing the whole fact tuples to multiple servers to construct a set of sibling cubes which can be merged together to obtain the whole cube. We employ a light weight distribution policy with fully duplicated dimension tables in each sibling server on the observation of very low proportion of space cost for dimension tables. OLAP query with distributed aggregate functions can be transformed into queries to be performed parallel in sibling servers. For non-distributed computing aggregate functions, such as median, the optimized median aggregate computing algorithm is proposed to reduce transmission volume between servers while computing the global median values. We also present a three-level framework in data warehouse to meet the requirement of shorter update period in "operational business intelligence". An asynchronous tunnel model is proposed to reduce update latency by pre-fetching updated tuples to OLAP processing server. Finally, we set up prototype system ParaCube to evaluate performance in SN (shared-nothing) system and multi-core platforms.