An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Materialized view selection and maintenance using multi-query optimization
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
A performance comparison of bitmap indexes
Proceedings of the tenth international conference on Information and knowledge management
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
QC-trees: an efficient summary structure for semantic OLAP
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Quotient cube: how to summarize the semantics of a data cube
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
High-dimensional OLAP: a minimal cubing approach
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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Data cube has been playing an essential role in OLAP (online analytical processing). The pre-computation of data cubes is critical for improving the response time of OLAP systems. However, as the size of data cube grows, the time it takes to perform this pre-computation becomes a significant performance bottleneck. In a high dimensional OLAP, it might not be practical to build all these cuboids and their indices. In this paper, we propose a parallel hierarchical cubing algorithm, based on an extension of the previous minimal cubing approach. The algorithm has two components: decomposition of the cube space based on multiple dimension attributes, and an efficient OLAP query engine based on a prefix bitmap encoding of the indices. This method partitions the high dimensional data cube into low dimensional cube segments. Such an approach permits a significant reduction of CPU and I/O overhead for many queries by restricting the number of cube segments to be processed for both the fact table and bitmap indices. The proposed data allocation and processing model support parallel I/O and parallel processing, as well as load balancing for disks and processors. Experimental results show that the proposed parallel hierarchical cubing method is significantly more efficient than other existing cubing methods.