Concept-oriented model: inference in hierarchical multidimensional space

TL;DR

This paper introduces a novel concept-oriented model for multidimensional data that integrates inference directly into analytical processes, enabling more complex and meaningful analysis beyond traditional numeric methods.

Contribution

It proposes a new approach to inference in multidimensional space using only axes and coordinates, and develops a query language with inference capabilities.

Findings

Inference can be integrated into multidimensional models using concept-oriented approach

Elementary operations enable constraint propagation and inference procedures

The query language with inference operator enhances analytical task solving

Abstract

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.