Aggregation Algorithms for Very Large Compressed Data Warehouses

Many efficient algorithms to compute multidimensional aggregation and Cube for relational OLAP have been developed. However, to our knowledge, there is nothing to date in the literature on aggregation algorithms on compressed data warehouses for multidimensional OLAP. This paper presents a set of aggregation algorithms on very large compressed data warehouses for multidimensional OLAP. These algorithms operate directly on compressed datasets without the need to first decompress them. They are applicable to data warehouses that are compressed using variety of data compression methods. The algorithms have different performance behavior as a function of dataset parameters, sizes of outputs and main memory availability. The analysis and experimental results show that the algorithms have better performance than the traditional aggregation algorithms.