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.

翻译：本文探讨了在数据立方体环境中评估查询趣味性的方法。我们假设存在一个存储数据立方体与层级结构的层次化多维数据库。首先，我们全面综述了人类行为研究与计算机科学领域的相关工作。我们将查询趣味性定义为沿新奇性、相关性、惊奇性、特异性等不同维度的分数向量，并辅以可用于评估各趣味性维度的信息分类体系加以完善。我们同时提供了句法层面（结果无关）的校验方法与外延层面（结果相关）的量化评估指标及算法，用于定量评估不同维度的趣味性。此外，我们还报告了所开展的用户研究结果，分析了各维度的重要性、随时间演化的规律以及研究参与者的行为特征。