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Issue No. 12 - December (2006 vol. 18)
ISSN: 1041-4347
pp: 1667-1680
Data compression is an effective technique to improve the performance of data warehouses. Since cube operation represents the core of online analytical processing in data warehouses, it is a major challenge to develop efficient algorithms for computing cube on compressed data warehouses. To our knowledge, very few cube computation techniques have been proposed for compressed data warehouses to date in the literature. This paper presents a novel algorithm to compute cubes on compressed data warehouses. The algorithm operates directly on compressed data sets without the need of first decompressing them. The algorithm is applicable to a large class of mapping complete data compression methods. The complexity of the algorithm is analyzed in detail. The analytical and experimental results show that the algorithm is more efficient than all other existing cube algorithms. In addition, a heuristic algorithm to generate an optimal plan for computing cube is also proposed
computational complexity, data compression, data mining, data warehouses,very large compressed data sets, data compression, compressed data warehouses, online analytical processing, cube computation techniques, algorithm complexity, heuristic algorithm,Data warehouses, Multidimensional systems, Costs, Algorithm design and analysis, Data compression, Databases, Data analysis, Heuristic algorithms, Decision making, Computer applications,Data warehouses, data compression, cube operation, OLAP.
"New Algorithm for Computing Cube on Very Large Compressed Data Sets", IEEE Transactions on Knowledge & Data Engineering, vol. 18, no. , pp. 1667-1680, December 2006, doi:10.1109/TKDE.2006.195
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