Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Optimization of constrained frequent set queries with 2-variable constraints
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Can we push more constraints into frequent pattern mining?
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient computation of Iceberg cubes with complex measures
SIGMOD '01 Proceedings of the 2001 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
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Multi-Tree Cubing algorithm for computing iceberg cubes
Journal of Intelligent Information Systems
| Hi-index | 0.00 |
Data cubes are an enabling approach for efficient on-line analytical processing (OLAP) systems. Iceberg cubes are special cubes consisting of the aggregates of multi-dimensional groups that satisfy user-specified thresholds. When there are a relatively large number of dimensions, the number of groups in an iceberg cube is huge. End users cannot fully understand the aggregate results or directly use them to make a decision. Approaches for the efficient computation of the top n groups have been proposed and have been shown to work well on iceberg cubes with simple aggregate functions such as COUNT and SUM. In this paper, we study the efficient computation of the top n groups with the complex aggregate function AVERAGE. As the average of a sub-group does not increase/decrease monotonically with its super-group, AVERAGE constraints cannot be used directly for pruning. We propose a new technique upper-bounding average which is anti-monotonic and can be used for low-cost effective pruning. Based on a tree structure representing groups, search and pruning techniques are developed, and an algorithm is proposed to compute the top n averages efficiently.