Stefano Rizzi

The Intentional Analytics Model (IAM) has been envisioned as a way to tightly couple OLAP and analytics. The IAM approach relies on two major cornerstones: (i) the user explores the data space by expressing her analysis intentions rather than by explicitly stating what data she needs, and (ii) in return she receives both multidimensional data and knowledge insights in the form of annotations of interesting subsets of data. 

As to (i), five intention operators are proposed, namely, describe, assess, explain, predict, and suggest. As to (ii), first-class citizens of the IAM are enhanced cubes, defined as multidimensional cubes coupled with highlights, i.e., sets of cube cells associated with interesting components of models extracted from cube.

The goal of the thesis is to provide a prototypical implementation of the explain and predict operators.