2022/12/06 by Dimos Gkitsakis, Gkitsakis, Dimos, Spyridon Kaloudis +9 · 1 citation
Computer Science · #Advanced Database Systems and Queries #Data Management and Algorithms #Data Visualization and Analytics #Databases (cs.DB) #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2212.03294
openalex publication_date 2022/12/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.