2018
Journal article
Open Access
Gastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions
Martinucci I., Natilli M., Lorenzoni V., Pappalardo L., Monreale A., Turchetti G., Pedreschi D., Marchi S., Barale R., De Bortoli N.
Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen.Source: BMC gastroenterology (Online) 18 (2018): 116. doi:10.1186/s12876-018-0832-9
DOI: 10.1186/s12876-018-0832-9
Project(s): SoBigData
Metrics:
Martinucci I., Natilli M., Lorenzoni V., Pappalardo L., Monreale A., Turchetti G., Pedreschi D., Marchi S., Barale R., De Bortoli N.
Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions through cashiers at university canteen.Source: BMC gastroenterology (Online) 18 (2018): 116. doi:10.1186/s12876-018-0832-9
DOI: 10.1186/s12876-018-0832-9
Project(s): SoBigData
Metrics:
See at:
bmcgastroenterol.biomedcentral.com 
| BMC Gastroenterology | BMC Gastroenterology | BMC Gastroenterology | Archivio della ricerca della Scuola Superiore Sant'Anna | DOAJ-Articles | ISTI Repository | CNR ExploRA
2018
Contribution to book
Open Access
Analyzing privacy risk in human mobility data
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Mobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.Source: , pp. 114–129, 2018
DOI: 10.1007/978-3-030-04771-9_10
Project(s): SoBigData
Metrics:
Pellungrini R., Pappalardo L., Pratesi F., Monreale A.
Mobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.Source: , pp. 114–129, 2018
DOI: 10.1007/978-3-030-04771-9_10
Project(s): SoBigData
Metrics:
See at:
Archivio della Ricerca - Università di Pisa | Lecture Notes in Computer Science 
| link.springer.com | CNR ExploRA
2018
Contribution to book
Open Access
How data mining and machine learning evolved from relational data base to data science
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
Amato G., Candela L., Castelli D., Esuli A., Falchi F., Gennaro C., Giannotti F., Monreale A., Nanni M., Pagano P., Pappalardo L., Pedreschi D., Pratesi F., Rabitti F., Rinzivillo S., Rossetti G., Ruggieri S., Sebastiani F., Tesconi M.
During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.Source: A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years, edited by Sergio Flesca, Sergio Greco, Elio Masciari, Domenico Saccà, pp. 287–306, 2018
DOI: 10.1007/978-3-319-61893-7_17
Metrics:
See at:
arpi.unipi.it | ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2018
Journal article
Open Access
Quantifying the relation between performance and success in soccer
Pappalardo L., Cintia P.
The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's position in a competition's final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team's performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.Source: Advances in Complex Systems 21 (2018). doi:10.1142/S021952591750014X
DOI: 10.1142/s021952591750014x
DOI: 10.48550/arxiv.1705.00885
Project(s): SoBigData
Metrics:
Pappalardo L., Cintia P.
The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team's position in a competition's final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team's performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems' view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.Source: Advances in Complex Systems 21 (2018). doi:10.1142/S021952591750014X
DOI: 10.1142/s021952591750014x
DOI: 10.48550/arxiv.1705.00885
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | Advances in Complex Systems | ISTI Repository | Advances in Complex Systems | doi.org | www.worldscientific.com | CNR ExploRA
2018
Report
Open Access
Open the black box data-driven explanation of black box decision systems
Pedreschi D., Giannotti F., Guidotti R., Monreale A., Pappalardo L., Ruggieri S., Turini F.
Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions:(i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation;(ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance;(iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.Source: ISTI Technical reports, 2018
Project(s): SoBigData
Pedreschi D., Giannotti F., Guidotti R., Monreale A., Pappalardo L., Ruggieri S., Turini F.
Black box systems for automated decision making, often based on machine learning over (big) data, map a user's features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases hidden in the algorithms, due to human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We introduce the local-to-global framework for black box explanation, a novel approach with promising early results, which paves the road for a wide spectrum of future developments along three dimensions:(i) the language for expressing explanations in terms of highly expressive logic-based rules, with a statistical and causal interpretation;(ii) the inference of local explanations aimed at revealing the logic of the decision adopted for a specific instance by querying and auditing the black box in the vicinity of the target instance;(iii), the bottom-up generalization of the many local explanations into simple global ones, with algorithms that optimize the quality and comprehensibility of explanations.Source: ISTI Technical reports, 2018
Project(s): SoBigData
See at:
arxiv.org | ISTI Repository | CNR ExploRA
2018
Journal article
Open Access
Effective injury forecasting in soccer with GPS training data and machine learning
Rossi A., Pappalardo L., Cintia P., Iaia F. M., Fernandez J., Medina D.
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.Source: PloS one 13 (2018): 1–15. doi:10.1371/journal.pone.0201264
DOI: 10.1371/journal.pone.0201264
DOI: 10.48550/arxiv.1705.08079
Project(s): SoBigData
Metrics:
Rossi A., Pappalardo L., Cintia P., Iaia F. M., Fernandez J., Medina D.
Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.Source: PloS one 13 (2018): 1–15. doi:10.1371/journal.pone.0201264
DOI: 10.1371/journal.pone.0201264
DOI: 10.48550/arxiv.1705.08079
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | PLoS ONE | PLoS ONE | PLoS ONE | PLoS ONE | ISTI Repository | doi.org | CNR ExploRA
2018
Conference article
Open Access
Weak nodes detection in urban transport systems: planning for resilience in Singapore
Ferretti M., Barlacchi G., Pappalardo L., Lucchini L., Lepri B.
The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc...). In this perspective we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Source: DSAA 2018 - IEEE 5th International Conference on Data Science and Advanced Analytics, pp. 472–480, 01-04 October 2018
DOI: 10.1109/dsaa.2018.00061
DOI: 10.48550/arxiv.1809.07839
Project(s): SoBigData
Metrics:
Ferretti M., Barlacchi G., Pappalardo L., Lucchini L., Lepri B.
The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc...). In this perspective we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.Source: DSAA 2018 - IEEE 5th International Conference on Data Science and Advanced Analytics, pp. 472–480, 01-04 October 2018
DOI: 10.1109/dsaa.2018.00061
DOI: 10.48550/arxiv.1809.07839
Project(s): SoBigData
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | ISTI Repository | doi.org | doi.org | ieeexplore.ieee.org | CNR ExploRA
2018
Journal article
Open Access
Data-driven generation of spatio-temporal routines in human mobility
Pappalardo L., Simini F.
The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.Source: Data mining and knowledge discovery 32 (2018): 787–829. doi:10.1007/s10618-017-0548-4
DOI: 10.1007/s10618-017-0548-4
DOI: 10.48550/arxiv.1607.05952
Project(s): CIMPLEX, Dynamic equation approach to forecast long-range demographic scenarios , SoBigData
Metrics:
Pappalardo L., Simini F.
The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.Source: Data mining and knowledge discovery 32 (2018): 787–829. doi:10.1007/s10618-017-0548-4
DOI: 10.1007/s10618-017-0548-4
DOI: 10.48550/arxiv.1607.05952
Project(s): CIMPLEX
, Dynamic equation approach to forecast long-range demographic scenarios , SoBigData Metrics:
See at:
arXiv.org e-Print Archive | Data Mining and Knowledge Discovery | Data Mining and Knowledge Discovery | link.springer.com | Data Mining and Knowledge Discovery | ISTI Repository | Explore Bristol Research | doi.org | CNR ExploRA
2018
Journal article
Open Access
Gravity and scaling laws of city to city migration
Prieto Curiel R., Pappalardo L., Gabrielli L., Bishop S. R.
Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.Source: PloS one 13 (2018): 1–19. doi:10.1371/journal.pone.0199892
DOI: 10.1371/journal.pone.0199892
Project(s): CIMPLEX, SoBigData
Metrics:
Prieto Curiel R., Pappalardo L., Gabrielli L., Bishop S. R.
Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.Source: PloS one 13 (2018): 1–19. doi:10.1371/journal.pone.0199892
DOI: 10.1371/journal.pone.0199892
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
PLoS ONE | PLoS ONE | ISTI Repository | CNR ExploRA