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Epidemics and city. How mobility and well being changed with COVID19 era
How did the COVID-19 epidemics change our mobility habits, and how did it impact on people’s well-being and on the virus transmissibility? In the first webinar of the seminar...-
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Efficiency - Effectiveness Trade-offs in Learning to Rank
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...-
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High Performance and Scalable Analytics Module
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...-
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Private Optimizing Empty Container Repositioning and Fleet Deployment via Configurabl...
We introduce a novel framework, Configurable SemiPOMDPs, to model this type of problems. Furthermore, we provide a two-stage learning algorithm, “Configure & Conquer”... -
Simulation of urban impact of navigation services
This experiment performs a simulation (using SUMO) of the urban impact of two navigation services (TomTom and OpenRoutingService) in the city of Milan, Italy. -
Synthetic Mobility Purpose Dataset
A synthetically generated dataset representing purpose-of-motion data in the format of individual mobility networks.-
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Kinematic Features of Porto Taxi Trips
This dataset comprises tabular information related to the movement of taxis in the city of Porto, Portugal. For every taxi journey, we segmented the trajectory into 20...-
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Shopping retail synthetic dataset (TVAE)
Synthetic shopping retail consumption data generated with TVAE. The dataset provides monthly information on the spending of synthetic customers belonging to two classes (i.e.,... -
Private 64-tiles tessellation of Manhattan
Squared tessellation of the Manhattan district of NYC. The manhattan land zone (including small island) are tessellated into 64 squared tiles of equal dimension. More details on... -
Private Synthetic Mobility Network of Chicago's bikes
AI-generated mobility network represented daily flows of bike sharing vehicles in Chicago. The mobility network represent the flow among the tiles of a 64-tiles squared... -
Private Synthetic Mobility Network of Chicago's taxis
AI-generated mobility network represented daily flows of taxi rides in Chicago. The mobility network represent the flow among the tiles of a 64-tiles squared tessellation of the... -
Private Synthetic Mobility Network of Manhattan's bikes
AI-generated mobility network represented daily flows of bike sharing vehicles in Manhattan. The mobility network represent the flow among the tiles of a 64-tiles squared... -
Private Synthetic Mobility Network of Manhattan's taxis
AI-generated mobility network represented daily flows of taxi rides in Manhattan. The mobility network represent the flow among the tiles of a 64-tiles squared tessellation of... -
Prediction of next career moves from scientific profiles
This is a two-stage predictive model for the mobility of scientists. First, data mining is used to predict which researcher will move in the next year on the basis of their... -
Car sharing dataset
The dataset comprises pickup and drop-off times and locations of vehicles in 10 European cities for one of the major free-floating car sharing operator. For nine of these... -
ISTAT Census zone Tuscany
This dataset contains the geometry of about 20.000 census sectors and limited demographic information of Tuscany region (Italy).-
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Private 64-tiles tessellation of Chicago
Squared tessellation of the city center of Chicago, Illinois, into 64 tiles. Tessellation only of the central part of Chicago, namely the neighborhoods 'LOOP', 'NEAR SOUTH... -
Ground truth evaluation of home location detection algorithms
Inferring mobile phone users' home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging... -
Influence of missing RR-Intervals caused by motion artifacts on HRV features ...
Wearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade,... -
Nowcasting well-being with human mobility data
This method describes an analytical framework to nowcast well-being by using mobility measures extracted from mobile phone data.