Interactive exploration and visualization of OLAP cubes
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.