Aggregation and cube are important operations for online analytical processing (OLAP). Many efficient algorithms to compute aggregation and cube for relational OLAP have been developed. Some work has been done on efficiently computing cube for multidimensional data warehouses that store data sets in multidimensional arrays rather than in tables. However, to our knowledge, there is nothing to date in the literature describing aggregation algorithms on compressed data warehouses for multidimensional OLAP. This paper presents a set of aggregation algorithms on compressed data warehouses for multidimensional OLAP. These algorithms operate directly on compressed data sets, which are compressed by the mapping-complete compression methods, without the need to first decompress them. The algorithms have different performance behaviors as a function of the data set parameters, sizes of outputs and main memory availability. The algorithms are described and the I/O and CPU cost functions are presented in this paper. A decision procedure to select the most efficient algorithm for a given aggregation request is also proposed. The analysis and experimental results show that the algorithms have better performance on sparse data than the previous aggregation algorithms.
|Original language||English (US)|
|Number of pages||15|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|State||Published - May 2002|
Bibliographical noteFunding Information:
This work is supported in part by the Natural Science Foundation of China under Grant No. 69873014 and in part by the 973 Plan of China through Grant No. G1999032704.
- Aggregation on compressed data warehouses
- Data warehouse
- Multidimensional array