Keynote Speakers

Prof. Alfredo Cuzzocrea
Founder and Director, Big Data Engineering and Analytics Laboratory (iDEA Lab)
University of Calabria, Rende, Italy

Alfredo Cuzzocrea is Distinguished Professor of Computer Engineering, and Founder and Director of the Big Data Engineering and Analytics Laboratory (iDEA Lab) of the University of Calabria, Rende, Italy. He also covers the role of Full Professor in Computer Engineering at the Department of Computer Science of the University of Paris City, Paris, France, as holding the Excellence Chair in Big Data Management and Analytics. He is Honorary Professor of Computer Engineering at the School of Engineering and Technology of the Amity University, Noida, India. He is also Research Associate of the National Research Council (CNR), Rome, Italy. Previously, he has covered the role of Full Professor in Computer Engineering at the Department of Computer Science, University of Lorraine, Nancy, France, where he held the Excellence Chair in Big Data Privacy and Cybersecurity. He is author or co-author of more than 900 papers in international conferences (including CIKM, EDBT, MDM, SSDBM, PAKDD, DOLAP), international journals (including TKDE, JCSS, IS, INS, JMLR, FGCS) and international books. He is recognized in prestigious international research rankings.

Speech Title: Multidimensional Supervised Learning over Big Data: Models, Definitions, and Solutions
Abstract: Supervised learning is an important task in Artificial Intelligence (AI) in various areas such as Computer Vision and Image Understanding, Data Mining (DM) and Knowledge Discovery, and so forth. In the era of big data, it faces critical challenges coming from the curse of dimensionality, heterogeneous data sources, and the need for scalable computation. To address these, Multidimensional Supervised Learning (MSL) has emerged as a formal paradigm that unifies multidimensional modeling with predictive analytics. This talk introduces theoretical foundations of MSL, along with rigorous definitions of multidimensional data, where facts, dimensions, hierarchies, and measures are explicitly represented to preserve structural and semantic richness.
Our approach for performing MSL over big data builds upon OLAP-based multidimensional modeling to organize large-scale datasets into interpretable and computationally efficient structures. On top of this modeling layer, we perform pattern discovery and pattern matching across data hierarchies to capture meaningful relationships and enhance predictive accuracy as well as intuitive visual exploration.
By formalizing definitions, developing models, and presenting scalable solutions, this speech positions multidimensional supervised learning as a basis for next-generation big data analytics.