Efficient Algorithms for On-line Analysis Processing On Compressed Data Warehouses

Data compression is an effective technique to improve the performance of data warehouses. Aggregation and cube are important operations for on-line analytical processing (OLAP). It is a major challenge to develop efficient algorithms for aggregation and cube operations on compressed data warehouses. Many efficient algorithms to compute aggregation and cube for relational OLAP have been developed. Some work has been done on efficiently computing aggregation and cube for multidimensional data warehouses (MDWs) that store datasets in multidimensional arrays rather than in tables. However, to our knowledge, there is few to date in the literature describing aggregation algorithms on compressed data warehouses for multidimensional OLAP.