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Aggregation Algorithms for Very Large Compressed Data Warehouses

Jianzhong Li, Doron Rotem, and Jaideep Srivastava

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Abstract
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.


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BIBTEX

@inproceedings{DBLP:conf/vldb/LiRS99,
  author    = {Jianzhong Li and
                Doron Rotem and
                Jaideep Srivastava},
   editor    = {Malcolm P. Atkinson and
                Maria E. Orlowska and
                Patrick Valduriez and
                Stanley B. Zdonik and
                Michael L. Brodie},
   title     = {Aggregation Algorithms for Very Large Compressed Data Warehouses},
   booktitle = {VLDB'99, Proceedings of 25th International Conference on Very
                Large Data Bases, September 7-10, 1999, Edinburgh, Scotland,
                UK},
   publisher = {Morgan Kaufmann},
   year      = {1999},
   isbn      = {1-55860-615-5},
   pages     = {651-662},
   crossref  = {DBLP:conf/vldb/99},
   bibsource = {DBLP, http://dblp.uni-trier.de} } },


























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