@conference {1038,
	title = {Recognizing Residents and Tourists with Retail Data Using Shopping Profiles},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers{\textquoteright} behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33\% of the customers they are responsible for more than 90\% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.},
	doi = {10.1007/978-3-319-76111-4_35},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_35},
	author = {Riccardo Guidotti and Lorenzo Gabrielli}
}
@inbook {636,
	title = {Retrieving Points of Interest from Human Systematic Movements},
	booktitle = {Software Engineering and Formal Methods},
	year = {2014},
	pages = {294{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers{\textquoteright} systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.},
	doi = {10.1007/978-3-319-15201-1_19},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
