Understanding peace through the world news
Voukelatou V., Miliou I., Giannotti F., Pappalardo L.
Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Source: EPJ 11 (2022). doi:10.1140/epjds/s13688-022-00315-z
DOI: 10.1140/epjds/s13688-022-00315-z
Project(s): XAI 
, SoBigData-PlusPlus
See at:
epjdatascience.springeropen.com 
| ISTI Repository | CNR ExploRA
Blood sample profile helps to injury forecasting in elite soccer players
Rossi A., Pappalardo L., Filetti C., Cintia P.
Purpose: By analyzing external workloads with machine learning models (ML), it is now possible to predict injuries, but with a moderate accuracy. The increment of the prediction ability is nowadays mandatory to reduce the high number of false positives. The aim of this study was to investigate if players' blood sample profiles could increase the predictive ability of the models trained only on external training workloads. Method: Eighteen elite soccer players competing in Italian league (Serie B) during the seasons 2017/2018 and 2018/2019 took part in this study. Players' blood samples parameters (i.e., Hematocrit, Hemoglobin, number of red blood cells, ferritin, and sideremia) were recorded through the two soccer seasons to group them into two main groups using a non-supervised ML algorithm (k-means). Additionally to external workloads data recorded every training or match day using a GPS device (K-GPS 10 Hz, K-Sport International, Italy), this grouping was used as a predictor for injury risk. The goodness of ML models trained were tested to assess the influence of blood sample profile to injury prediction. Results: Hematocrit, Hemoglobin, number of red blood cells, testosterone, and ferritin were the most important features that allowed to profile players and to analyze the response to external workloads for each type of player profile. Players' blood samples' characteristics permitted to personalize the decision-making rules of the ML models based on external workloads reaching an accuracy of 63%. This approach increased the injury prediction ability of about 15% compared to models that take into consideration only training workloads' features. The influence of each external workload varied in accordance with the players' blood sample characteristics and the physiological demands of a specific period of the season. Conclusion: Field experts should hence not only monitor the external workloads to assess the status of the players, but additional information derived from individuals' characteristics permits to have a more complete overview of the players well-being. In this way, coaches could better personalize the training program maximizing the training effect and minimizing the injury risk.Source: Sport sciences for health (Testo stamp.) (2022). doi:10.1007/s11332-022-00932-1
DOI: 10.1007/s11332-022-00932-1
Project(s): SoBigData-PlusPlus
See at:
link.springer.com | ISTI Repository | CNR ExploRA
Gross polluters and vehicle emissions reduction
Bohm M., Nanni M., Pappalardo L.
Vehicle emissions produce an important share of a city's air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing the full driving cycle of vehicles, or focus on a few vehicles. We have used GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of private vehicles in three European cities. We found that the emissions across the vehicles and roads are well approximated by heavy-tailed distributions and thus discovered the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are far more effective than those limiting circulation based on an uninformed choice of vehicles. Our study contributes to shaping the discussion on how to measure emissions with digital data.Source: Nature sustainability (2022). doi:10.1038/s41893-022-00903-x
DOI: 10.1038/s41893-022-00903-x
Project(s): Track and Know , HumanE-AI-Net , SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA | www.nature.com
Introduction to the special issue on social mining and big data ecosystem for open, responsible data science
Pappalardo L., Grossi V., Pedreschi D.
Source: International Journal of Data Science and Analytics (Online) (2021). doi:10.1007/s41060-021-00253-5
DOI: 10.1007/s41060-021-00253-5
Project(s): SoBigData , SoBigData-PlusPlus
See at:
link.springer.com | ISTI Repository | CNR ExploRA | International Journal of Data Science and Analytics 
| International Journal of Data Science and Analytics
STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several scientific areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus on the spatial and temporal dimensions of mobility only, while the social dimension is often neglected. On other hand, models that embed a social mechanism have trivial or unrealistic spatial and temporal mechanisms. We propose STS-EPR, a mechanistic model that captures the spatial, temporal, and social dimensions of human mobility together. Our results show that STS-EPR generates realistic trajectories, making it better than models that lack either in the social, the spatial, or the temporal mechanisms. STS-EPR is a step towards the design of mechanistic models that can capture all the aspects of human mobility in a comprehensive way.Source: Procedia computer science 184 (2021): 258–265. doi:10.1016/j.procs.2021.03.035
DOI: 10.1016/j.procs.2021.03.035
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA | www.sciencedirect.com
Evaluation of home detection algorithms on mobile phone data using individual-level ground truth
Pappalardo L., Ferres L., Sacasa M., Cattuto C., Bravo L.
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 mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.Source: EPJ 10 (2021). doi:10.1140/epjds/s13688-021-00284-9
DOI: 10.1140/epjds/s13688-021-00284-9
Project(s): SoBigData-PlusPlus
See at:
epjdatascience.springeropen.com | ISTI Repository | CNR ExploRA
Explaining the difference between men's and women's football
Pappalardo L., Rossi A., Natilli M., Cintia P.
Women's football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men's football. While the two sports are often compared based on the players' physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match's playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men's and women's football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier's decisions. The differences between men's and women's football are rooted in play accuracy, the recovery time of ball possession, and the players' performance quality. Our methodology may help journalists and fans understand what makes women's football a distinct sport and coaches design tactics tailored to female teams.Source: PloS one 16 (2021). doi:10.1371/journal.pone.0255407
DOI: 10.1371/journal.pone.0255407
Project(s): SoBigData-PlusPlus
See at:
journals.plos.org | ISTI Repository | CNR ExploRA
Living in a pandemic: adaptation of individual mobility and social activity in the US
Lucchini L., Centellegher S., Pappalardo L., Gallotti R., Privitera F., Lepri B., De Nadai M.
The non-pharmaceutical interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, has dramatically influenced our behaviour in everyday life. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the COVID-19 pandemic and the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals in four US states and empirically show the dramatic disruption in people's life. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines and reducing person-to-person contact activities. Moreover, we show that the stringency of interventions alone does explain the number and duration of visits to venues: individual patterns of visits seem to be influenced by the local severity of the pandemic and a risk adaptation factor, which increases the people's mobility regardless of the stringency of interventions.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
Coach2vec: autoencoding the playing style of soccer coaches
Cintia P., Pappalardo L.
Capturing the playing style of professional soccer coaches is a complex, and yet barely explored, task in sports analytics. Nowadays, the availability of digital data describing every relevant spatio-temporal aspect of soccer matches, allows for capturing and analyzing the playing style of players, teams, and coaches in an automatic way. In this paper, we present coach2vec, a workflow to capture the playing style of professional coaches using match event streams and artificial intelligence. Coach2vec extracts ball possessions from each match, clusters them based on their similarity, and reconstructs the typical ball possessions of coaches. Then, it uses an autoencoder, a type of artificial neural network, to obtain a concise representation (encoding) of the playing style of each coach. Our experiments, conducted on soccer-logs describing the last four seasons of the Italian first division, reveal interesting similarities between prominent coaches, paving the road to the simulation of playing styles and the quantitative comparison of professional coaches.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
Understanding peacefulness through the world news
Voukelatou V., Miliou I., Giannotti F., Pappalardo L.
Peacefulness is a principal dimension of well-being for all humankind and is the way out of inequity and every single form of violence. Thus, its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed the research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use the SHAP methodology to obtain the most important variables that drive the predictions. This analysis highlights each country's profile and provides explanations for the predictions overall, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by Social Good researchers, policy-makers, and peace-builders, with data science tools as powerful as machine learning, could contribute to maximize the societal benefits and minimize the risks to peacefulness.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
Improving vehicles' emissions reduction policies by targeting gross polluters
Böhm M., Nanni M., Pappalardo L.
Vehicles' emissions produce a significant share of cities' air pollution, with a substantial impact on the environment and human health. Traditional emission estimation methods use remote sensing stations, missing vehicles' full driving cycle, or focus on a few vehicles. This study uses GPS traces and a microscopic model to analyse the emissions of four air pollutants from thousands of vehicles in three European cities. We discover the existence of gross polluters, vehicles responsible for the greatest quantity of emissions, and grossly polluted roads, which suffer the greatest amount of emissions. Our simulations show that emissions reduction policies targeting gross polluters are way more effective than those limiting circulation based on a non-informed choice of vehicles. Our study applies to any city and may contribute to shaping the discussion on how to measure emissions with digital data.Source: ISTI Research Report, SoBigData++, 2021
Project(s): SoBigData-PlusPlus
See at:
arxiv.org | ISTI Repository | CNR ExploRA
Quantifying the presence of air pollutants over a road network in high spatio-temporal resolution
Böhm M., Nanni M., Pappalardo L.
Monitoring air pollution plays a key role when trying to reduce its impact on the environment and on human health. Traditionally, two main sources of information about the quantity of pollutants over a city are used: monitoring stations at ground level (when available), and satellites' remote sensing. In addition to these two, other methods have been developed in the last years that aim at understanding how traffic emissions behave in space and time at a finer scale, taking into account the human mobility patterns. We present a simple and versatile framework for estimating the quantity of four air pollutants (CO2, NOx, PM, VOC) emitted by private vehicles moving on a road network, starting from raw GPS traces and information about vehicles' fuel type, and use this framework for analyses on how such pollutants distribute over the road networks of different cities.Source: NeurIPS 2020 Workshop - Tackling Climate Change with Machine Learning, Online conference, 11/12/2020
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA | www.climatechange.ai
A mechanistic data-driven approach to synthesize human mobility considering the spatial, temporal, and social dimensions together
Cornacchia G., Pappalardo L.
Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.Source: ISPRS international journal of geo-information 10 (2021). doi:10.3390/ijgi10090599
DOI: 10.3390/ijgi10090599
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA | www.mdpi.com
A deep gravity model for mobility flows generation
Simini F., Barlacchi G., Luca M., Pappalardo L.
The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model's prediction with explainable AI techniques.Source: Nature communications 12 (2021). doi:10.1038/s41467-021-26752-4
DOI: 10.1038/s41467-021-26752-4
Project(s): SoBigData-PlusPlus
See at:
ISTI Repository | CNR ExploRA | www.nature.com
Automatic pass annotation from soccer video streams based on object detection and LSTM
Sorano D., Carrara F., Cintia P., Falchi F., Pappalardo L.
Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of data that describe all the spatio-temporal events that occur in each match. These events (e.g., passes, shots, fouls) are collected by human operators manually, constituting a considerable cost for data providers in terms of time and economic resources. In this paper, we describe PassNet, a method to recognize the most frequent events in soccer, i.e., passes, from video streams. Our model combines a set of artificial neural networks that perform feature extraction from video streams, object detection to identify the positions of the ball and the players, and classification of frame sequences as passes or not passes. We test PassNet on different scenarios, depending on the similarity of conditions to the match used for training. Our results show good classification results and significant improvement in the accuracy of pass detection with respect to baseline classifiers, even when the match's video conditions of the test and training sets are considerably different. PassNet is the first step towards an automated event annotation system that may break the time and the costs for event annotation, enabling data collections for minor and non-professional divisions, youth leagues and, in general, competitions whose matches are not currently annotated by data providers.Source: ECML PKDD 2020 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 475–490, Ghent, Belgium, 14-18/09/2020
DOI: 10.1007/978-3-030-67670-4_29
Project(s): SoBigData , SoBigData-PlusPlus
See at:
ISTI Repository | link.springer.com | link.springer.com | CNR ExploRA
Living in a pandemic: changes in mobility routines, social activity and adherence to COVID-19 protective measures
Lucchini L., Centellegher S., Pappalardo L., Gallotti R., Privitera F., Lepri B., De Nadai M.
Non-Pharmaceutical Interventions (NPIs), aimed at reducing the diffusion of the COVID-19 pandemic, have dramatically influenced our everyday behaviour. In this work, we study how individuals adapted their daily movements and person-to-person contact patterns over time in response to the NPIs. We leverage longitudinal GPS mobility data of hundreds of thousands of anonymous individuals to empirically show and quantify the dramatic disruption in people's mobility habits and social behaviour. We find that local interventions did not just impact the number of visits to different venues but also how people experience them. Individuals spend less time in venues, preferring simpler and more predictable routines, also reducing person-to-person contacts. Moreover, we find that the individual patterns of visits are influenced by the strength of the NPIs policies, the local severity of the pandemic and a risk adaptation factor, which increases the people's mobility regardless of the stringency of interventions. Finally, despite the gradual recovery in visit patterns, we find that individuals continue to keep person-to-person contacts low. This apparent conflict hints that the evolution of policy adherence should be carefully addressed by policymakers, epidemiologists and mobility experts.Source: Scientific reports (Nature Publishing Group) 11 (2021). doi:10.1038/s41598-021-04139-1
DOI: 10.1038/s41598-021-04139-1
Project(s): SoBigData-PlusPlus
See at:
Scientific Reports | Scientific Reports | Scientific Reports | ISTI Repository | CNR ExploRA | www.nature.com
A narrative review for a machine learning application in sports: an example based on injury forecasting in soccer
Rossi A., Pappalardo L., Cintia P.
In the last decade, the number of studies about machine learning algorithms applied to sports, e.g., injury forecasting and athlete performance prediction, have rapidly increased. Due to the number of works and experiments already present in the state-of-the-art regarding machine-learning techniques in sport science, the aim of this narrative review is to provide a guideline describing a correct approach for training, validating, and testing machine learning models to predict events in
sports science. The main contribution of this narrative review is to highlight any possible strengths and limitations during all the stages of model development, i.e., training, validation, testing, and interpretation, in order to limit possible errors that could induce misleading results. In particular, this paper shows an example about injury forecaster that provides a description of all the features that could be used to predict injuries, all the possible pre-processing approaches for time series analysis, how to correctly split the dataset to train and test the predictive models, and the importance to explain the decision-making approach of the white and black box models.Source: Sports (Basel) 10 (2021). doi:10.3390/sports10010005
DOI: 10.3390/sports10010005
Project(s): SoBigData-PlusPlus
See at:
Sports | ISTI Repository | CNR ExploRA | Sports
Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020
DOI: 10.32079/isti-tr-2020/005
See at:
ISTI Repository | CNR ExploRA
(So) Big Data and the transformation of the city
Andrienko G., Andrienko N., Boldrini C., Caldarelli G., Cintia P., Cresci S., Facchini A., Giannotti F., Gionis A., Guidotti R., Mathioudakis M., Muntean C. I., Pappalardo L., Pedreschi D., Pournaras E., Pratesi F., Tesconi M., Trasarti R.
The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the "City of Citizens" thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.Source: International Journal of Data Science and Analytics (Print) 1 (2020). doi:10.1007/s41060-020-00207-3
DOI: 10.1007/s41060-020-00207-3
Project(s): SoBigData , SoBigData-PlusPlus
See at:
Aaltodoc Publication Archive | HELDA - Digital Repository of the University of Helsinki | Archivio istituzionale della ricerca - Università degli Studi di Venezia Ca' Foscari | link.springer.com | International Journal of Data Science and Analytics | City Research Online | ISTI Repository | Fraunhofer-ePrints | CNR ExploRA | International Journal of Data Science and Analytics | Archivio della Ricerca - Università di Pisa | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics
Measuring objective and subjective well-being: Dimensions and data sources
Voukelatou V., Gabrielli L., Miliou I., Cresci S., Sharma R., Tesconi M., Pappalardo L.
Well-being is an important value for people's lives, and it could be considered as an index of societal progress. Researchers have suggested two main approaches for the overall measurement of well-being, the objective and the subjective well-being. Both approaches, as well as their relevant dimensions, have been traditionally captured with surveys. During the last decades, new data sources have been suggested as an alternative or complement to traditional data. This paper aims to present the theoretical background of well-being, by distinguishing between objective and subjective approaches, their relevant dimensions, the new data sources used for their measurement and relevant studies. We also intend to shed light on still barely unexplored dimensions and data sources that could potentially contribute as a key for public policing and social development.Source: International Journal of Data Science and Analytics (Print) (2020). doi:10.1007/s41060-020-00224-2
DOI: 10.1007/s41060-020-00224-2
Project(s): SoBigData
See at:
link.springer.com | International Journal of Data Science and Analytics | ISTI Repository | CNR ExploRA | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics | International Journal of Data Science and Analytics