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2015
Conference article
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Managing travels with PETRA: The Rome use case
Botea A., Braghin S., Lopes N., Guidotti R., Pratesi F.
The aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.Source: 31st IEEE International Conference on Data Engineering. Data Mining and Smart Cities Applications Workshop, pp. 110–111, Seoul, Korea, 13-17/04/2015
DOI: 10.1109/icdew.2015.7129558
Project(s): PETRA
Metrics:
Botea A., Braghin S., Lopes N., Guidotti R., Pratesi F.
The aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.Source: 31st IEEE International Conference on Data Engineering. Data Mining and Smart Cities Applications Workshop, pp. 110–111, Seoul, Korea, 13-17/04/2015
DOI: 10.1109/icdew.2015.7129558
Project(s): PETRA
Metrics:
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doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2015
Conference article
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Mobility Mining for Journey Planning in Rome
Berlingerio M., Bicer V., Botea A., Braghin S., Lopes N., Guidotti R., Pratesi F.
We present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional "bus lines", available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.Source: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. European Conference, pp. 222–226, Porto, Portugal, 07-11/09/2015
DOI: 10.1007/978-3-319-23461-8_18
Project(s): PETRA
Metrics:
Berlingerio M., Bicer V., Botea A., Braghin S., Lopes N., Guidotti R., Pratesi F.
We present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional "bus lines", available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.Source: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases. European Conference, pp. 222–226, Porto, Portugal, 07-11/09/2015
DOI: 10.1007/978-3-319-23461-8_18
Project(s): PETRA
Metrics:
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doi.org | link.springer.com | CNR ExploRA
2015
Contribution to book
Open Access
Retrieving points of interest from human systematic movements
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
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' 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.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 294–308, 2015
DOI: 10.1007/978-3-319-15201-1_19
Project(s): PETRA
Metrics:
Guidotti R., Monreale A., Rinzivillo S., Pedreschi D., Giannotti F.
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' 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.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 294–308, 2015
DOI: 10.1007/978-3-319-15201-1_19
Project(s): PETRA
Metrics:
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ISTI Repository 
| doi.org | link.springer.com | CNR ExploRA
2015
Conference article
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Social or green? A datadriven approach for more enjoyable carpooling
Guidotti R., Sassi A., Berlingerio M., Pascale A.
Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent works on carpooling and journey planning take into account, besides mobility match, also social aspects and, more generally, non-monetary rewards. In line with this, we presenta data-driven methodology for a more enjoyable carpooling. We introduce a measure of enjoyability based on people's interests,social links, and tendency to connect to people with similar or dissimilar interests. We devise a methodology to compute enjoyability from crowd-sourced data, and we show how this can be used on real world datasets to optimize for both mobility and enjoyability. Our methodology was tested on real data from Rome and San Francisco. We compare the results of an optimization model minimizing the number of cars, and a greedy approach maximizing the enjoyability. We evaluate them in terms of cars saved, and average enjoyability of the system. We present also the results of a user study, with more than 200 users reporting an interest of 39% in the enjoyable solution. Moreover, 24%of people declared that sharing the car with interesting people would be the primary motivation for carpooling.Source: 18th IEEE Intelligent Transportation Systems Conference, pp. 842–847, Las Palmas de Gran Canaria, Spain, 15-18/09/2015
DOI: 10.1109/itsc.2015.142
Project(s): PETRA
Metrics:
Guidotti R., Sassi A., Berlingerio M., Pascale A.
Carpooling, i.e. the sharing of vehicles to reach common destinations, is often performed to reduce costs and pollution. Recent works on carpooling and journey planning take into account, besides mobility match, also social aspects and, more generally, non-monetary rewards. In line with this, we presenta data-driven methodology for a more enjoyable carpooling. We introduce a measure of enjoyability based on people's interests,social links, and tendency to connect to people with similar or dissimilar interests. We devise a methodology to compute enjoyability from crowd-sourced data, and we show how this can be used on real world datasets to optimize for both mobility and enjoyability. Our methodology was tested on real data from Rome and San Francisco. We compare the results of an optimization model minimizing the number of cars, and a greedy approach maximizing the enjoyability. We evaluate them in terms of cars saved, and average enjoyability of the system. We present also the results of a user study, with more than 200 users reporting an interest of 39% in the enjoyable solution. Moreover, 24%of people declared that sharing the car with interesting people would be the primary motivation for carpooling.Source: 18th IEEE Intelligent Transportation Systems Conference, pp. 842–847, Las Palmas de Gran Canaria, Spain, 15-18/09/2015
DOI: 10.1109/itsc.2015.142
Project(s): PETRA
Metrics:
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doi.org | ieeexplore.ieee.org | CNR ExploRA
2015
Conference article
Restricted
Find your way back: Mobility profile mining with constraints
Kotthoff L., Nanni M., Guidotti R., Òsullivan B.
Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.Source: Principles and Practice of Constraint Programming. 21st International Conference, pp. 638–653, Cork, Ireland, 31/09/2015-04/10/2015
DOI: 10.1007/978-3-319-23219-5_44
Project(s): ICON
Metrics:
Kotthoff L., Nanni M., Guidotti R., Òsullivan B.
Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.Source: Principles and Practice of Constraint Programming. 21st International Conference, pp. 638–653, Cork, Ireland, 31/09/2015-04/10/2015
DOI: 10.1007/978-3-319-23219-5_44
Project(s): ICON
Metrics:
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doi.org | www.scopus.com | CNR ExploRA
2015
Conference article
Restricted
Interaction prediction in dynamic networks exploiting community discovery
Rossetti G., Guidotti R., Pennacchioli D., Pedreschi D., Giannotti F.
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 553–558, Paris, France, 25-28/08/2015
DOI: 10.1145/2808797.2809401
Project(s): CIMPLEX
Metrics:
Rossetti G., Guidotti R., Pennacchioli D., Pedreschi D., Giannotti F.
Due to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.Source: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 553–558, Paris, France, 25-28/08/2015
DOI: 10.1145/2808797.2809401
Project(s): CIMPLEX
Metrics:
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dl.acm.org | doi.org | CNR ExploRA
2015
Conference article
Open Access
Behavioral entropy and profitability in retail
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344821
Project(s): CIMPLEX, SoBigData
Metrics:
Guidotti R., Coscia M., Pedreschi D., Pennacchioli D.
Human behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer's behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.Source: IEEE International Conference on Data Science and Advanced Analytics, Paris, France, 19-21/10/2015
DOI: 10.1109/dsaa.2015.7344821
Project(s): CIMPLEX
, SoBigData Metrics:
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www.michelecoscia.com | doi.org | ieeexplore.ieee.org | CNR ExploRA