Explaining Differences in Multidimensional Aggregates
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Intelligent Rollups in Multidimensional OLAP Data
Proceedings of the 27th International Conference on Very Large Data Bases
Using Datacube Aggregates for Approximate Querying and Deviation Detection
IEEE Transactions on Knowledge and Data Engineering
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
View Discovery in OLAP Databases through Statistical Combinatorial Optimization
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
CineCubes: cubes as movie stars with little effort
Proceedings of the sixteenth international workshop on Data warehousing and OLAP
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OLAP applications are widely used by business analysts as a decision support tool. While exploring the cube, end-users are rapidly confronted by analyzing 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. This paper fits in the scope of discovery-driven dynamic exploration. It presents a method coupling OLAP technologies and mining techniques to facilitate the whole process of exploration of the data cube by identifying the most relevant dimensions to expand. At each step of the process, a built-in rank on dimensions is restituted to the users. It is performed through indicators computed on the fly according to the user-defined data selection. A proof of the implementation of this concept on the Oracle 10gsystem is described at the end of the paper.