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Author: Alexandr Savinov

Affiliation: SAP Research, Germany

ISBN: 978-989-8565-18-1

Keyword(s): Multidimensional Data Models, Data Analytics, Concept-oriented Model, Inference, Query Languages.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Data Analytics ; Data Engineering ; Dimensional Modeling ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; OLAP and MDA Models ; Symbolic Systems

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.

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Paper citation in several formats:
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

@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},
}

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

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