Empowering Multidimensional Machine Learning over Cloud- Enabled Big Data Infrastructures with ClustCube

Alfredo Cuzzocrea, Alfredo Cuzzocrea, Carmine Gallo, Marco Mastratisi

2024

Abstract

Multidimensional Machine Learning is emerging as one of the key features in the whole Big Data Analytics landscape. Within this broad context, the OLAP paradigm is a reference pillar, and it represents the theoretical and methodological foundation of the so-called Multidimensional Big Data Analytics trend, an emerging trend in the Big Data era. In this paper, we show how the state-of-the-art ClustCube framework, which predicates the marriage between OLAP and Clustering methodologies, can be successfully used and exploited for effectively and efficiently supporting Multidimensional Big Data Analytics in real-life big data applications and systems.

Paper Citation

in Harvard Style

Cuzzocrea A., Gallo C. and Mastratisi M. (2024). Empowering Multidimensional Machine Learning over Cloud- Enabled Big Data Infrastructures with ClustCube. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 389-396. DOI: 10.5220/0012727100003690

in Bibtex Style

@conference{iceis24,
author={Alfredo Cuzzocrea and Carmine Gallo and Marco Mastratisi},
title={Empowering Multidimensional Machine Learning over Cloud- Enabled Big Data Infrastructures with ClustCube},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={389-396},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012727100003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Empowering Multidimensional Machine Learning over Cloud- Enabled Big Data Infrastructures with ClustCube
SN - 978-989-758-692-7
AU - Cuzzocrea A.
AU - Gallo C.
AU - Mastratisi M.
PY - 2024
SP - 389
EP - 396
DO - 10.5220/0012727100003690
PB - SciTePress