Assessment Methods for the Interestingness of Cube Queries

Dimos Gkitsakis, Spyridon Kaloudis, Eirini Mouselli, Veronika Peralta, Patrick Marcel, Panos Vassiliadis

Summary

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 focus our approach on a taxonomy of the dimensions of interestingness, and specifically, relevance, surprise, novelty, and peculiarity. We propose specific measures and algorithms for assessing the different dimensions of cube query interestingness in a quantitative fashion.

Texts

Dimos Gkitsakis, Spyridon Kaloudis, Eirini Mouselli, Veronika Peralta, Patrick Marcel, Panos Vassiliadis. Assessment Methods for the Interestingness of Cube Queries. 25th International Workshop on Design, Optimization, Languages and Analytical Processing of Big Data (DOLAP 2023), March 28, 2023, Ioannina, Greece.
Local copy of the paper (PDF) [CEUR-WS proceedings]

Dimos Gkitsakis, Spyridon Kaloudis, Eirini Mouselli, Veronika Peralta, Patrick Marcel, Panos Vassiliadis. Cube Interestingness: Novelty, Relevance, Peculiarity and Surprise, arXiv, https://doi.org/10.48550/arXiv.2212.03294, 2022. Available at https://arxiv.org/abs/2212.03294. (Long version of the paper at arxiv)

Presentations

Plz., refer to our OLAP III page for a general overview of our research program.
  • A presentation of the paper (PDF)

    • A 18' video presentation is available as a youtube video.