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
Range queries in dynamic OLAP data cubes
Data & Knowledge Engineering
Modeling Multidimensional Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
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
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Hierarchical Prefix Cubes for Range-Sum Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Probabilistic Optimization of Top N 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
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
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
Extracting k most important groups from data efficiently
Data & Knowledge Engineering
Pruning attribute values from data cubes with diamond dicing
IDEAS '08 Proceedings of the 2008 international symposium on Database engineering & applications
Evaluation of top-k OLAP queries using aggregate r–trees
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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In decision support systems, having knowledge on the top k values is more informative and crucial than the maximum value. Unfortunately, the naive method involves high computational cost and the existing methods for range-max query are inefficient if applied directly. In this paper, we propose a Pre-computed Partition Top method (PPT) to partition the data cube and pre-store a number of top values for improving query performance. The main focus of this study is to find the optimum values for two parameters, i.e., the partition factor (b) and the number of pre-stored values (r), through analytical approach. A cost function based on Poisson distribution is used for the analysis. The analytical results obtained are verified against simulation results. It is shown that the PPT method outperforms other alternative methods significantly when proper b and r are used.