Towards Big OLAP Data Cube Classification Methodologies: The ClassCube Framework

Alfredo Cuzzocrea, Alfredo Cuzzocrea, Mojtaba Hajian

2025

Abstract

Focusing on the emerging big data analytics scenario, this paper introduces ClassCube, an innovative methodology that combines OLAP analysis and classification algorithms for improving effectiveness, expressive power and accuracy of the main classification task over big datasets shaped in the form of big OLAP data cubes. The key idea of ClassCube relies on dimensionality reduction tools, which are deeply investigated in this paper.

Paper Citation

in Harvard Style

Cuzzocrea A. and Hajian M. (2025). Towards Big OLAP Data Cube Classification Methodologies: The ClassCube Framework. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 351-356. DOI: 10.5220/0013448200003929

in Bibtex Style

@conference{iceis25,
author={Alfredo Cuzzocrea and Mojtaba Hajian},
title={Towards Big OLAP Data Cube Classification Methodologies: The ClassCube Framework},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2025},
pages={351-356},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013448200003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Towards Big OLAP Data Cube Classification Methodologies: The ClassCube Framework
SN - 978-989-758-749-8
AU - Cuzzocrea A.
AU - Hajian M.
PY - 2025
SP - 351
EP - 356
DO - 10.5220/0013448200003929
PB - SciTePress