面向概念的模型：层次多维空间中的推理
Concept-oriented model: inference in hierarchical multidimensional space
摘要：
尽管它的根本重要性，推理一直没有多维模型和分析应用程序的固有功能。这些模型主要针对数字（定量）分析，其中推理和语义的概念没有很好地定义。在本文中，我们认为，推理可以而且应该是多维数据模型和分析应用程序的组成部分。它演示了如何推理可以定义只使用多维的术语，如轴和坐标，而不是使用基于逻辑的方法。我们提出了一种新的方法，在多维空间的基础上，面向概念的数据模型的推理，并引入基本操作，然后用于定义约束传播和推理过程。我们描述了一种带有推理算子的查询语言，并证明了它在解决复杂分析任务中的有用性。
英文摘要：
In spite of its fundamental importance, inference has not been an inherent function of multidimensional models and analytical applications. These models are mainly aimed at numeric (quantitative) analysis where the notions of inference and semantics are not well defined. In this paper we argue that inference can be and should be integral part of multidimensional data models and analytical applications. It is demonstrated how inference can be defined using only multidimensional terms like axes and coordinates as opposed to using logic-based approaches. We propose a novel approach to inference in multidimensional space based on the concept-oriented model of data and introduce elementary operations which are then used to define constraint propagation and inference procedures. We describe a query language with inference operator and demonstrate its usefulness in solving complex analytical tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
