Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached...

Federated learning is a decentralized machine learning technique that aggregates partial models trained by a set of clients on their own private data to obtain a global model....

Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the...

Information technology allows us to generate, store, and process huge quantities of data. Search engines, satellites, sensor networks, scientists, security agencies,...

Artificial intelligence, and machine learning (ML) in particular, is being used for different purposes that are critical for human life. To avoid an algorithm-based...

In this seminar cycle, students are guided in the direct usage of a powerful C++ library implementing many state-of-the-art compressed data structures for big data. Other than...

This article describes an emergent logic of accumulation in the networked sphere, ‘surveillance capitalism,’ and considers its implications for ‘information civilization.’ The...

The rise of “Big Data” analytics in the private sector poses new challenges for privacy advocates. Through its reliance on existing data and predictive analysis to create...

Starting from an analysis of the EU Reg. n. 2016/679 on General Data Protection Regulation (GDPR), the Author deals with the opportunity to translate the current strategies on...

Slide from the Summer School on Computational Misinformation Analysis 2019 lesson

Study, design and analysis of IR systems which are efficient and effective to process, mine, search, cluster and classify bigdata document collections, coming from textual as...

This tutorial provides an 'Introduction to Learning to Rank' and focuses on 'Dealing with the Efficiency/Effectiveness trade-off in Web Search'. Moreover, it provides two...

The training material provided by the University of Tartu is about the Business Data Analytics course. This course is meant for students as a hands-on experience for solving...

Web archives are typically very broad in scope and extremely large in scale. This makes data analysis appear daunting, especially for non-computer scientists. These...

The goals of this module are: - Have a general knowledge of text mining problems and methods. - Recognize situations in which Sentiment Analysis techniques can solve...

The 'Database Module' aims to introduce database analysis, focusing on DBMS architecture, Relational Models, SQL language and SQL nested queries. It is part of the Master in...

An Apache Spark framework for easy data processing, extraction as well as derivation for archival collections. Originally developed for the use with Web archives, it has now...

The module aims to teach how to present the knowledge extracted from big data using multimedia story telling. It also shows some of the most recent and meaningful experiences...

Mining with big data or big data mining has become an active research area. Running current analytical methodologies and software tools on a single personal computer cannot...

This module provides an introduction to information storage and management performed in order to support business decisions of organizations. It is part of the Master in Big...