Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
ICDT '01 Proceedings of the 8th International Conference on Database Theory
Fast Computation of Sparse Datacubes
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
An intelligent customer retention system
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
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In this paper, we focus on an approach to On-Line Analytical Processing (OLAP) that is based on a database operator and data structure called the datacube. The datacube is a relational operator that is used to construct all possible views of a given data set. Efficient algorithms for computing the entire datacube - both sequentially and in parallel - have recently been proposed. However, due to space and time constraints, the assumption that all 2d (where d = dimensions) views should be computed is often not valid in practice. As a result, algorithms for computing partial datacubes are required. In this paper, we describe a parallel algorithm for computing partial datacubes and provide preliminary experimental results based on an implementation in C and MPI.