Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Towards the building of a dense-region-based OLAP system
Data & Knowledge Engineering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Dynamic Update Cube for Range-sum Queries
Proceedings of the 27th International Conference on Very Large Data Bases
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Online Aggregates in Dense-Region-Based Data Cube Representations
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
Efficient online aggregates in dense-region-based data cube representations
Transactions on large-scale data- and knowledge-centered systems II
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A data cube provides aggregate information to support a class of queries such as a range-sum query. To process those queries efficiently, some auxiliary information, i.e. prefix sums, is pre-computed and maintained. In reality however most of high dimensional data cubes are very sparse, causing a serious space overhead. In this paper, we investigate an algorithm that extracts dense sub-cubes from a large sparse cube based on the density function. Instead of maintaining a large prefix-sum cube, a few dense sub-cubes are maintained to reduce the space overhead and to restrict the update propagation. We present an iterative method that identifies dense intervals in each dimension and constructs sub-cubes based on the intervals found. We show the effectiveness of our method through the analytic comparison and experiment with respect to various data sets and dimensions.