Cubegrades: Generalizing Association Rules
Data Mining and Knowledge Discovery
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
Ad-Hoc Association-Rule Mining within the Data Warehouse
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 8 - Volume 8
Using Datacube Aggregates for Approximate Querying and Deviation Detection
IEEE Transactions on Knowledge and Data Engineering
Efficient multidimensional data representations based on multiple correspondence analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Built-in indicators to automatically detect interesting cells in a cube
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
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In large companies, Online Analytical Processing (OLAP) technologies are widely used by business analysts as a decision-support tool. The exploration of the data is performed using operators such as drill-down, roll-up or slice. While exploring the cube, end-users are rapidly confronted with analysing a huge number of drill-paths according to the different dimensions. Generally, analysts are only interested in a small part of them, which corresponds to either high statistical associations between dimensions or atypical cell values. Moreover, identifying the most interesting cells is a matter for business analysts. Coupling OLAP technologies and mining methods may help them by the automation of this tedious task. This paper, in the scope of discovery-driven exploration, presents a method to facilitate the whole process of exploration of the data cube by identifying the most relevant dimensions to expand. A built-in rank on dimensions is displayed, at each step of the process, to the users, who are still free to choose the right dimension to expand for their analysis. Built-in rank on dimensions is performed through indicators computed on the fly according to the user-defined data selection. We present how this methodology offers a support to the decision-making, directly integrated to a commercial OLAP management system. A proof of concept implementation on the ORACLE 10g system is described at the end of the paper.