The purpose of the course is to introduce the main analysis techniques for spatio-temporal data, with a particular focus on human mobility (including vehicles), aimed to better understand the overall mobility of a territory. The presentation will be supported by several case studies developed with the SoBigData.eu laboratory.
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 of journalism and story telling based on quantitative information extracted from different data sources.
The module aims to teach the software modules used to build a modern search engine and the analysis of the performance and the computational limits of the algorithmics solutions currently used in each of them. Practical and theoretical fundmentals for the organization and the analysis of IR systems.
Gianbiagio Curato received his master degree in Theoretical Physics in 2010 from the University of Florence doing a thesis about nonlinear neural networks dynamics. He holds a Ph.D. in Mathematics for Finance issued by Scuola Normale Superiore in 2015. He has worked as IT Analyst for Unicredit Business Integrated Solutions. He is, currently, a post doctoral researcher at Scuola Normale Superiore. His main research interests are: information diffusion, social network analysis, time series analysis, market microstructure, optimal execution.
Born in Salerno (Italy), I earned my PhD in Computer Science at University of Pisa with the thesis "Human Mobility, Social Networks and Economic Development: a Data Science perspective". In my research, I exploit the power of Big Data to study many aspects of human behavior: the patterns of human mobility, the structure and evolution of complex networks, the patterns of success in sports, and the usage of data-driven measures of human behavior to monitor and predict the economic development of countries, cities, and territories.
Ugo Scaiella is Developer Team Leader at SpazioDati, a startup with offices in Pisa and Trento that develops products for sales intelligence and business information by leveraging big-data and AI technologies. He leads the R&D department for text analytics, machine learning, and information retrieval. Before, he served for 3 years as research fellow at Dipartimento di Informatica of University of Pisa, where he coauthored several conference papers with Prof.
Angelica Lo Duca is a postdoctoral researcher at the Institute of Informatics and Telematics of the National Research Council of Pisa. In 2012, she received her Ph.D. in Ingegneria dell'Informazione from the University of Pisa. She received her Bachelor's and Master's degrees in Computer Engineering from University of Pisa respectively in 2005 and 2007. Currently, she works at the Web Applications for the Future Internet Laboratory, in the Semantic Web and Data Visualization group.
The module aims to show the main characteristics of the innovation processes in companies and institutions. After some basics of innovation economics, the management of the innovation processes will be presented (role of R&D, Open Innovation, etc.). The module also shows new innovation opportunities available after the last progresses in large scale data acquisition and elaboration, the basics of business models and start-ups. An exercise of business model innovation will try to explore che big data potential in opening new business opportunities.
The module presents technologies and systems for designing, populating and querying Data Warehouse for decision support. The emphasis is on technologies and analysis of application problems by using examples and case studies. The student will acquire knowledge and skills on major technologies for Business Intelligence such as ETL (Extract, Transform and Load), Data Warehousing, Analytics SQL, OLAP (Online Analytical Processing).
The module aims to present the basic methods and techniques for the visualization and presentation of the information obtained from different sources: structured data (relational, hierarchical, trees), network data (social network), temporal data, spatial data and spatio-temporal data. Studying the existing methods and tools, some scenarios of visual analytics will be presented.
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |