Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
EDBT '00 Proceedings of the 7th International Conference on Extending Database Technology: Advances in Database Technology
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Hierarchical Compact Cube for Range-Max Queries
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Range-Max/Min Query in OLAP Data Cube
DEXA '00 Proceedings of the 11th International Conference on Database and Expert Systems Applications
Range Sum Queries in Dynamic OLAP Data Cubes
CODAS '01 Proceedings of the Third International Symposium on Cooperative Database Systems for Advanced Applications
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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A range query applies an aggregation operation over all selected cells of an OLAP data cube where selection is specified by the range of contiguous values for each dimension. Many works have focused on efficiently computing range sum or range max queries. Most of these algorithms use a uniformly partitioning scheme for the data cube. In this paper, we improve on query costs of some of these existing algorithms by noting two key areas. First, end-user range queries usually involve repetitive query patterns, which provide a variable sized partitioning scheme that can be used to partition the data cubes. Query costs are reduced because pre-computation is retrieved for entire partitions, rather than computed for a partial region in many partitions, which requires large amounts of cell accesses to the data cube. Second, data in the data cube can be arranged such that each partition is stored in as few physical storage blocks as possible, thus reducing the I/O costs for answering range queries.