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Authors: Arnaud Castelltort and Anne Laurent

Affiliation: UMR5506 - Université Montpellier 2, France

Keyword(s): OLAP Analysis, NoSQL Graph Databases.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Symbolic Systems

Abstract: OLAP is a leading technology for analysing data and decision making. It helps the users to discover relevant information from large databases. Graph OLAP has been studied for several years in the OLAP framework. In existing work, the authors study how to import graph data into OLAP cube models but no work has explored yet the feasability to exploit graph structures to store analytical data. As graph databases are more and more used through NoSQL implementations (e.g., social and biological networks), in this paper we aim at providing an original model for managing cubes into NoSQL graphs. We show how cubes can be represented in graphs and how these structures can then be used for graph OLAP queries to support decision making.

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Paper citation in several formats:
Castelltort, A. and Laurent, A. (2014). NoSQL Graph-based OLAP Analysis. In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR; ISBN 978-989-758-048-2; ISSN 2184-3228, SciTePress, pages 217-224. DOI: 10.5220/0005072902170224

@conference{kdir14,
author={Arnaud Castelltort. and Anne Laurent.},
title={NoSQL Graph-based OLAP Analysis},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR},
year={2014},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005072902170224},
isbn={978-989-758-048-2},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (IC3K 2014) - KDIR
TI - NoSQL Graph-based OLAP Analysis
SN - 978-989-758-048-2
IS - 2184-3228
AU - Castelltort, A.
AU - Laurent, A.
PY - 2014
SP - 217
EP - 224
DO - 10.5220/0005072902170224
PB - SciTePress