The Intentional Analytics Model (IAM) has been recently envisioned as a new paradigm to couple OLAP and analytics. It relies on two basic ideas: (i) letting the user explore data by expressing her analysis intentions rather than the data she needs, and (ii) returning enhanced cubes, i.e., multidimensional data annotated with knowledge insights in the form of interesting model components (e.g., clusters). In this paper we contribute to give a proof-of-concept for the IAM vision by delivering an end-to-end implementation of describe, one of the five intention operators introduced by IAM. Among the research challenges left open in IAM, those we address are (i) automatically tuning the size of models (e.g., the number of clusters), (ii) devising a measure to estimate the interestingness of model components, (iii) selecting the most effective chart or graph for visualizing each enhanced cube depending on its features, and (iv) devising a visual metaphor to display enhanced cubes and interact with them. We assess the validity of our approach in terms of user effort for formulating intentions, effectiveness, efficiency, and scalability.

意向分析模型 (IAM) 最近被设想为耦合 OLAP 和分析的新范式。它依赖于两个基本思想：（i）让用户通过表达她的分析意图而不是她需要的数据来探索数据，以及（ii）返回增强的多维数据集，即以有趣的模型组件的形式用知识见解注释的多维数据（例如，集群）。在本文中，我们通过提供describe的端到端实现来为 IAM 愿景提供概念验证。，IAM 引入的五个意图算子之一。在 IAM 中未解决的研究挑战中，我们要解决的挑战是（i）自动调整模型的大小（例如，集群的数量），（ii）设计一种方法来估计模型组件的兴趣度，（iii）选择可视化每个增强立方体的最有效图表或图形，具体取决于其特征，以及 (iv) 设计一个视觉隐喻来显示增强立方体并与之交互。我们根据用户制定意图、有效性、效率和可扩展性的努力来评估我们方法的有效性。