Data mining: concepts and techniques
Data mining: concepts and techniques
CubiST: a new algorithm for improving the performance of ad-hoc OLAP queries
Proceedings of the 3rd ACM international workshop on Data warehousing and OLAP
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Total
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Advanced visualization for OLAP
DOLAP '03 Proceedings of the 6th ACM international workshop on Data warehousing and OLAP
Interactive Visual Exploration of Multidimensional Data: Requirements for CommonGIS with OLAP
DEXA '04 Proceedings of the Database and Expert Systems Applications, 15th International Workshop
Constraining and summarizing association rules in medical data
Knowledge and Information Systems
Proceedings of the ACM 11th international workshop on Data warehousing and OLAP
Fast and dynamic OLAP exploration using UDFs
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Evaluating statistical tests on OLAP cubes to compare degree of disease
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
From analysis to interactive exploration: building visual hierarchies from OLAP cubes
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Association rule discovery with the train and test approach for heart disease prediction
IEEE Transactions on Information Technology in Biomedicine
DOLAP 2011: overview of the 14th international workshop on data warehousing and olap
Proceedings of the 20th ACM international conference on Information and knowledge management
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An OLAP cube is typically explored with multiple aggregations selecting different subsets of cube dimensions to analyze trends or to discover unexpected results. Unfortunately, such analytic process is generally manual and fails to statistically explain results. In this work, we propose to combine dimension lattice traversal and parametric statistical tests to identify significant metric differences between cube cells. We present a 2D interactive visualization of the OLAP cube based on a checkerboard that enables isolating and interpreting significant measure differences between two similar cuboids, which differ in one dimension and have the same values on the remaining dimensions. Cube exploration and visualization is performed by automatically generated SQL queries. An experimental evaluation with a medical data set presents statistically significant results and interactive visualizations, which link risk factors and degree of disease.