OLAP, relational, and multidimensional database systems
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An efficient processing of range-MIN/MAX queries over data cube
Information Sciences: an International Journal
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
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Adaptive Method for Range Top- k Queries in OLAP Data Cubes
DEXA '02 Proceedings of the 13th International Conference on Database and Expert Systems Applications
Dynamic top-k range reporting in external memory
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
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A range top k query finds the top k maximum values over all selected cells of an OLAP data cube where the selection is specified by the range of contiguous values for each dimension. In this paper, we propose a partition-based storage structure, which is capable of answering both range top and bottom k queries in OLAP sparse data cubes. This is achieved by partitioning a multi-dimensional sparse data cube and storing it in partition-major order into two one-dimensional arrays: one is for the dense partitions and the other one is for the sparse partitions. This algorithm supports both single cell update and bulk batch update. Nevertheless, the update cost for a set of cells in a partition is similar to the update cost of a single cell update, i.e. extra 2 I/Os in the most cases and the worst is extra 5 I/Os in some very rare cases. Our approach also reduces the storage cost of sparse data cubes.