Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 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
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
pCube: Update-Efficient Online Aggregation with Progressive Feedback and Error Bounds
SSDBM '00 Proceedings of the 12th International Conference on Scientific and Statistical Database Management
Relative Prefix Sums: An Efficient Approach for Querying Dynamic OLAP Data Cubes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Space-Efficient Data Cubes for Dynamic Environments
Space-Efficient Data Cubes for Dynamic Environments
ProPolyne: A Fast Wavelet-Based Algorithm for Progressive Evaluation of Polynomial Range-Sum Queries
EDBT '02 Proceedings of the 8th International Conference on Extending Database Technology: Advances in Database Technology
Optimal Range Max Datacube for Fixed Dimensions
ICDT '03 Proceedings of the 9th International Conference on Database Theory
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Data cubes provide aggregate information to support the analysis of the contents of data warehouses and databases. An important tool to analyze data in data cubes is the range query. For range queries that summarize large regions of massive data cubes, computing the query result on-the-fly can result in non-interactive response times. To speed up range queries, values that summarize regions of the data cube are pre-computed and stored. This faster response time results in more expensive updates and/or space overhead. While the emphasis is typically on low query and update costs, growing data collections increase the demand for space-efficient approaches. In this paper two techniques are presented that have the same update and query costs as earlier approaches, without introducing any space overhead.