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Tesi etd-10112018-134117


Thesis type
Tesi di dottorato di ricerca
Author
MILIOU, IOANNA
URN
etd-10112018-134117
Title
Big Data Analytics for Nowcasting and Forecasting Social Phenomena
Settore scientifico disciplinare
INF/01
Corso di studi
INFORMATICA
Commissione
tutor Prof. Pedreschi, Dino
correlatore Dott. Rinzivillo, Salvatore
Parole chiave
  • Nowcasting
  • Big Data Analytics
  • Forecasting
Data inizio appello
22/10/2018;
Consultabilità
parziale
Data di rilascio
22/10/2021
Riassunto analitico
One of the most pressing, and fascinating challenges of our time is understanding the complexity<br>of the global interconnected society we inhabit. This connectedness reveals in many<br>phenomena: in the rapid growth of the Internet and Web, in the ease with which global communication<br>and trade now takes place, and in the ability of news and information as well as<br>epidemics, trends, financial crises and social unrest to spread around the world with surprising<br>speed and intensity. Ours is also a time of opportunity to observe and measure how our society<br>intimately works: Big Data originating from the digital breadcrumbs of human activities promise<br>to let us scrutinize the ground truth of individual and collective behavior at an unprecedented<br>detail in real time. Multiple dimensions of our social life have Big Data proxies nowadays. We<br>can use Big Data, as signals, as proxies for forecast and nowcast different phenomena, and even<br>more social phenomena. We can manage to describe and predict how humans and society works.<br>We can use geolocated data to observe and measure the behavior of a population, to build better<br>cities tailored to the movement of the population, with lower commuting times and lower<br>pollution. We can exploit medical data to build classifiers able to help in diagnosing and curing<br>diseases. We can use industrial data to improve the production processes, and create smarter<br>and more secure factories. We can do a lot of other incredible and useful things with the support<br>of data and analytical tools able to extract useful knowledge from raw data.<br>In this thesis we introduce data-driven as well as model-driven approaches to predict different<br>phenomena, from epidemics to socio-economic attraction. We use Big Data deriving from our<br>everyday life as external proxies to nowcast and forecast the evolution of phenomena whose study<br>relies only on historical data or data that come only with a significant lag. We use supermarket<br>retail data as an external signal in order to predict the curve of an internal time series, the<br>influenza one. When the flu season arrives, people are starting to get sick. Getting sick affects<br>their everyday life and behavior. This change in behavior should propagate in their purchases<br>in the supermarket. So they will buy products that will reflect the fact that they are sick.<br>We also study human movements that are inherently massive, dynamical, and complex. But<br>understanding the individual mobility patterns, could be of such a fundamental importance for<br>so many different phenomena. We decided to exploit these patterns in order to study and predict<br>the attraction of different socio-economic factors of human environment. In our first approach<br>we study the distribution of the travelling sub-populations in Tuscany region in Italy, to the<br>airports of the region and we built a dynamic model for the interplay of attraction of availability<br>of air travel and an airport’s popularity among the population. Based on this model, we forecast<br>the future evolution of the airports in the region. In our second approach, we identifiy and<br>categorize industrial clusters in Veneto region in Italy, by size and population dynamics and<br>measured their attraction. We create a real-time system which help us to feel the pulse of a city,<br>and predict the rise of new industrial clusters or the death of existing ones. Finally, we attempt<br>prediction in social networks, introducing the interaction prediction problem, trying to predict<br>intra-community interactions, interactions that may occur in the interior of the same community,<br>and we applied the same approach to predict inter-community interactions, the weak links that<br>keep together the modular structure composing complex networks.
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