# A Subjective Interestingness measure for Business Intelligence explorations

## TL;DR

This paper introduces a new subjective interestingness measure for Business Intelligence exploration that automatically infers user beliefs from past interactions to better evaluate query relevance.

## Contribution

It proposes a novel method to model user belief based on past data interactions and defines a subjective interestingness measure for multidimensional queries.

## Key findings

- The measure correlates with user behavior in experiments.
- Query parts effectively infer user belief.
- The approach works on both simulated and real data.

## Abstract

This paper addresses the problem of defining a subjective interestingness measure for BI exploration. Such a measure involves prior modeling of the belief of the user. The complexity of this problem lies in the impossibility to ask the user about the degree of belief in each element composing their knowledge prior to the writing of a query. To this aim, we propose to automatically infer this user belief based on the user's past interactions over a data cube, the cube schema and other users past activities. We express the belief under the form of a probability distribution over all the query parts potentially accessible to the user, and use a random walk to learn this distribution. This belief is then used to define a first Subjective Interestingness measure over multidimensional queries. Experiments conducted on simulated and real explorations show how this new subjective interestingness measure relates to prototypical and real user behaviors, and that query parts offer a reasonable proxy to infer user belief.