Using Regression to Explain Cube Measures

The Intentional Analytics Model (IAM) has been devised to couple OLAP and analytics by (i) letting users express their analysis intentions on multidimensional data cubes and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of models (e.g., correlations). Five intention operators were proposed to this end; of these, \sf{describe} and \sf{assess} have been investigated in previous papers. In this work we enrich the IAM picture by focusing on the \sf{explain} operator, whose goal is to provide an answer to the user asking ``why does measure $m$ show these values?''. Specifically, we propose a syntax for the operator and discuss how enhanced cubes are built by (i) finding the polynomials that best approximate the relationship between $m$ and the other cube measures, and (ii) highlighting the most interesting one. Finally, we test the operator implementation in terms of efficiency.