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
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 Discover Interesting Drill Paths in a Cube
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Embedded indicators to facilitate the exploration of a data cube
International Journal of Business Intelligence and Data Mining
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
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In large companies, On-Line Analytical Processing (OLAP) technologies are widely used by business analysts as a decision support tool. Nevertheless, while exploring the cube, analysts are rapidly confronted by analyzing a huge number of visible cells to identify the most interesting ones. Coupling OLAP technologies and mining methods may help them by the automation of this tedious task. In the scope of discovery-driven exploration, this paper presents two methods to detect and highlight interesting cells within a cube slice. The cell's degree of interest is based on the calculation of either test-value or Chi-Square contribution. Indicators are computed instantaneously according to the user-defined dimensions drill-down. Their display is done by a colorcoding system. A proof of concept implementation on the ORACLE 10g system is described at the end of the paper.