# Cube Interestingness: Novelty, Relevance, Peculiarity and Surprise

arXiv: https://arxiv.org/abs/2212.03294 · HTML: https://mlsanity.com/paper/2212.03294/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2022.

## Built on by (most cited first, 1 of 1)

- [Unveiling Interesting Insights: Monte Carlo Tree Search for Knowledge Discovery](https://mlsanity.com/paper/2510.00876/) (2025)

## Abstract (arXiv)

In this paper, we discuss methods to assess the interestingness of a query in an environment of data cubes. We assume a hierarchical multidimensional database, storing data cubes and level hierarchies. We start with a comprehensive review of related work in the fields of studies of human behavior and computer science. We define the interestingness of a query as a vector of scores along difference dimensions, like novelty, relevance, surprise and peculiarity and complement this definition with a taxonomy of the information that can be used to assess each of these dimensions of interestingness. We provide both syntactic (result-independent) checks and extensional (result-dependent) measures and algorithms for assessing the different dimensions of interestingness in a quantitative fashion. We also report our findings on a user study that we conducted, analyzing the significance of each dimension, its evolution over time and the behavior of the study's participants.