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下评估查询兴趣度的方法。我们假设存在一个存储数据立方体和层级结构的多维层次数据库。首先全面综述了人类行为研究与计算机科学领域的相关工作。我们将查询兴趣度定义为沿不同维度的得分向量，涵盖新颖性、相关性、意外性和特异性等维度，并通过用于评估各兴趣度维度的信息分类体系补充这一定义。我们提供了语法层面（与结果无关）的检验方法，以及外延层面（与结果相关）的量度标准与算法，用于定量评估不同维度的兴趣度。我们还报告了所开展的用户研究结果，分析了每个维度的重要性、其随时间演变的情况以及研究参与者的行为特征。