2022-2023

The module aims at preparing students to the approprieted presentation of data and knowledge extracted from them through visualization tools and narratives that exploit multimedia.
The module first presents the basic visualization techniques for the effective presentation of information from several different sources: structured data (relational, hierarchies, trees), relational data (social networks), temporal data, spatial data and spatio-temporal data.

The module si composed by several Seminars on experiences and case- and use-studies of Big Data analytics and Social Mining from the SoBigData.eu labs and from the companies and institutions that are partners in the Master.

The information circulating on the web and social networks is increasingly multimedia in nature. The possibility to understand the content and to search for multimedia documents on a large scale, expecially in the absence of textual descriptions, has become a strategic tool. The module aims to present tools for analyzing and extracting information from multimedia data, in order to search them in huge databases. This module is based on hands-on work.

This module introduces the main methods of analysis and mining of opinions and personal evaluations for users based on Big Data generated on the web or other sources. Emphasis will be put on text mining method applied to text originated on social media. Lessons will be supported by case studies developed in the SoBigData.eu lab.

The module provides the description of a search engine structure and of Text Mining tools, by analyzing their characteristics and limits with respect to the computational cost, the precision/recall/F1 parameters, and the expressivity of the supported queries. The module is also based on hands-on activities that will present well-known open-source Python tools for the crawling and analysis of web pages, the semantic annotation of texts (TagMe), and the indexing of text data collections (ElasticSearch).

The course will first introduce the fundamentals of artificial neural networks and, then, it will provide an overview of the main techniques and models of the deep learning field. Specific focus will be placed on detailing neural models that are useful for addressing predictive tasks on vectorial, sequential and image data, and to generative deep learning, including variational and adversarial learning.

This module presentes techniques and methods for acquisition of Big Data from a large sources of data available, including mobile phone data, GPS data, customer purchase data, social network data, open and administrative data, environmental and personal sensor data. We discuss also several participatory methods for crowdsourcing or crowdsensing collection of data through ad hoc campains like serious games and viral diffusion.

The module aims to introduce ethical and legal notions of privacy, anonymity, transparency and non-discrimination, also referring the Directives and Regulations of the European Union and their ongoing evolution. The module will show technologies for Privacy-by-Design, for predictive model auditing and for protecting the users' rights and that allow the analysis of Big Data without harming the right to the protection of personal data, to transparency and to a fair treatment.