Implementing data cubes efficiently
SIGMOD '96 Proceedings of the 1996 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
Simultaneous optimization and evaluation of multiple dimensional queries
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Mining Multi-Dimensional Constrained Gradients in Data Cubes
Proceedings of the 27th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Optimizing multiple dimensional queries simultaneously in multidimensional databases
The VLDB Journal — The International Journal on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
| Hi-index | 0.00 |
Analysts often explore data cubes to identify anomalous regions that may represent problem areas or new opportunities. Discovery-driven exploration (proposed by S. Sarawagi et al [5]) automatically detects and marks the exceptions for the user and reduces the reliance on manual discovery. However, when the data is large, it is hard to materialize the whole cube due to the limitations of both space and time. So, exploratory mining on complete cube cells needs to construct the data cube dynamically. That will take a very long time. In this paper, we investigate optimization methods by pushing several constraints into the mining process. By enforcing several user-defined constraints, we first restrict the multidimensional space to a small constrained-cube and then mine exceptions on it. Two efficient constrained-cube construction algorithms, the NAIVE algorithm and the AGOA algorithm, were proposed. Experimental results indicate that this kind of constraint-based exploratory mining method is efficient and scalable.