Junior Assistant Professor of Computer Science and Engineering University of Bologna

Matteo Francia is a Junior Assistant Professor at the University of Bologna, where he got his Ph.D. in Computer Science and Engineering.
His teaching subjects are big data, cloud platforms, and principles of informatics.
His research focuses on advanced big data analytics, with particular reference to databases, mobility data, and precision agriculture.
He authored publications in international conferences and journals, including Information Systems, Information Systems Frontiers, IEEE TKDE, DOLAP, ADBIS, ER, and EDBT, where he was awarded the best demo paper in 2021.
He served as program chair at DATAPLAT (2022-2024) and as guest editor for the Future Generation Computer Systems and Information System Frontiers international journals.
He collaborated with several companies in the fields of mobility data and precision agriculture.
He was a visiting scholar at The University of Queensland in 2019.
He received the MSc and BSc with honors from the University of Bologna in 2017 and 2014, respectively.
He was awarded by Rotary in 2018 and 2017 as the best graduate in the School of Engineering and Architecture and in Computer Science and Engineering, respectively.


Sviluppo di un percorso con le principali tecnologie in ambito analytics: dalla preparazione dei dati grezzi all’analisi OLAP e al Machine Learning

This course introduces the basic principles and methods of Business Intelligence with emphasis on Data Warehousing, OLAP analysis, and social intelligence. It also focuses on how Business Intelligence solutions can be designed according to user requirements and desires and includes a hands-on session with OLAP tools (mainly Power BI).

Data Marketing and Analytics

This course is a natural continuation of the Machine Learning course. It provides guidelines for running a Data Mining process and then discusses, with practical examples, the complete pipeline from data to machine learning concepts.
In particular, the following topics are covered:

  • The functions of Data Mining.
  • Supervised and unsupervised learning.
  • Algorithms and methods for constructing classification models.
  • Clustering algorithms.
  • Algorithms for discovering association rules.
  • Methods for evaluating the quality of data mining results.

Principles are introduced in class with presentation of slides and stimulating discussions with students. Methods are then applied with laboratory exercises.


Data Science and Business Analytics
Artificial Intelligence and Innovation Management