Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An array-based algorithm for simultaneous multidimensional aggregates
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 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
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth 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
Mining confident rules without support requirement
Proceedings of the tenth international conference on Information and knowledge management
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Computing Iceberg Queries Efficiently
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Mining Frequent Itemsets Using Support Constraints
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
On the Computation of Multidimensional Aggregates
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Efficient Computation of Iceberg Cubes by Bounding Aggregate Functions
IEEE Transactions on Knowledge and Data Engineering
Mining constraint-based patterns using automatic relaxation
Intelligent Data Analysis
Rough Set Model for Constraint-based Multi-dimensional Association Rule Mining
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
The Multi-Tree Cubing algorithm for computing iceberg cubes
Journal of Intelligent Information Systems
Strategies for complex data cube queries
Applied Intelligence
Multiway pruning for efficient iceberg cubing
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
| Hi-index | 0.00 |
The iceberg cube mining computes all cells v, corresponding to GROUP BY partitions, that satisfy a given constraint on aggregated behaviors of the tuples in a GROUP BY partition. The number of cells often is so large that the result cannot be realistically searched without pushing the constraint into the search. Previous works have pushed antimonotone and monotone constraints. However, many useful constraints are neither antimonotone nor monotone. We consider a general class of aggregate constraints of the form f(v)\theta \sigma, where f is an arithmetic function of SQL-like aggregates and \theta is one of . We propose a novel pushing technique, called Divide-and-Approximate, to push such constraints. The idea is to recursively divide the search space and approximate the given constraint using antimonotone or monotone constraints in subspaces. This technique applies to a class called separable constraints, which properly contains all constraints built by an arithmetic function f of all SQL aggregates.