Inference in Hierarchical Multidimensional Space

Alexandr Savinov

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 analysis where the notion of inference is not well defined. In this paper we define inference using only multidimensional terms like axes and coordinates as opposed to using logic-based approaches. We propose an inference procedure which is based on a novel formal setting of nested partially ordered sets with operations of projection and de-projection.

References

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Paper Citation


in Harvard Style

Savinov A. (2012). Inference in Hierarchical Multidimensional Space . In Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA, ISBN 978-989-8565-18-1, pages 70-76. DOI: 10.5220/0004039000700076


in Bibtex Style

@conference{data12,
author={Alexandr Savinov},
title={Inference in Hierarchical Multidimensional Space},
booktitle={Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,},
year={2012},
pages={70-76},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004039000700076},
isbn={978-989-8565-18-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Data Technologies and Applications - Volume 1: DATA,
TI - Inference in Hierarchical Multidimensional Space
SN - 978-989-8565-18-1
AU - Savinov A.
PY - 2012
SP - 70
EP - 76
DO - 10.5220/0004039000700076