<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Cornacchia, Giuliano</style></author><author><style face="normal" font="default" size="100%">Böhm, Matteo</style></author><author><style face="normal" font="default" size="100%">Giovanni Mauro</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How Routing Strategies Impact Urban Emissions</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 30th International Conference on Advances in Geographic Information Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1145/3557915.3560977</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Association for Computing Machinery</style></publisher><pub-location><style face="normal" font="default" size="100%">New York, NY, USA</style></pub-location><isbn><style face="normal" font="default" size="100%">9781450395298</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Navigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Andrienko, Gennady</style></author><author><style face="normal" font="default" size="100%">Andrienko, Natalia</style></author><author><style face="normal" font="default" size="100%">Boldrini, Chiara</style></author><author><style face="normal" font="default" size="100%">Conti, Marco</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Bertoli, Simone</style></author><author><style face="normal" font="default" size="100%">Jisu Kim</style></author><author><style face="normal" font="default" size="100%">Muntean, Cristina Ioana</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Passarella, Andrea</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Pollacci, Laura</style></author><author><style face="normal" font="default" size="100%">Francesca Pratesi</style></author><author><style face="normal" font="default" size="100%">Sharma, Rajesh</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human migration: the big data perspective</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics</style></secondary-title><short-title><style face="normal" font="default" size="100%">International Journal of Data Science and Analytics</style></short-title></titles><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020/03/23</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/article/10.1007%2Fs41060-020-00213-5</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">1–20</style></pages><isbn><style face="normal" font="default" size="100%">2364-4168</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">How can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Gianni Barlacchi</style></author><author><style face="normal" font="default" size="100%">Roberto Pellungrini</style></author><author><style face="normal" font="default" size="100%">Filippo Simini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Human Mobility from theory to practice: Data, Models and Applications</style></title><secondary-title><style face="normal" font="default" size="100%">Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.1145/3308560.3320099</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Riccardo Guidotti</style></author><author><style face="normal" font="default" size="100%">Soldani, Jacopo</style></author><author><style face="normal" font="default" size="100%">Neri, Davide</style></author><author><style face="normal" font="default" size="100%">Brogi, Antonio</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Helping your docker images to spread based on explainable models</style></title><secondary-title><style face="normal" font="default" size="100%">Joint European Conference on Machine Learning and Knowledge Discovery in Databases</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007/978-3-030-10997-4_13</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Docker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: https://github.com/di-unipi-socc/DockerImageMiner.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Amato, G.</style></author><author><style face="normal" font="default" size="100%">Candela, L.</style></author><author><style face="normal" font="default" size="100%">Castelli, D.</style></author><author><style face="normal" font="default" size="100%">Esuli, A.</style></author><author><style face="normal" font="default" size="100%">Falchi, F.</style></author><author><style face="normal" font="default" size="100%">Gennaro, C.</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Pagano, P.</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Francesca Pratesi</style></author><author><style face="normal" font="default" size="100%">Rabitti, F.</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author><author><style face="normal" font="default" size="100%">Sebastiani, F.</style></author><author><style face="normal" font="default" size="100%">Tesconi, M.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Flesca, Sergio</style></author><author><style face="normal" font="default" size="100%">Greco, Sergio</style></author><author><style face="normal" font="default" size="100%">Masciari, Elio</style></author><author><style face="normal" font="default" size="100%">Saccà, Domenico</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science</style></title><secondary-title><style face="normal" font="default" size="100%">A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">Cham</style></pub-location><pages><style face="normal" font="default" size="100%">287 - 306</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-61893-7</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Leonardo Candela</style></author><author><style face="normal" font="default" size="100%">Paolo Manghi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Valerio Grossi</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures</style></title><secondary-title><style face="normal" font="default" size="100%">D-Lib Magazine</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1045/january2017-candela</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1/2</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Research e-infrastructures are &quot;systems of systems&quot;, patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between &quot;business process modeling languages&quot;, which offer a formal and high-level description of a reasoning, protocol, or procedure, and &quot;workflow execution languages&quot;, which enable the fully automated execution of a sequence of computational steps via dedicated engines.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Riivo Kikas</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Marlon Dumas</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Homophilic network decomposition: a community-centric analysis of online social services</style></title><secondary-title><style face="normal" font="default" size="100%">Social Network Analysis and Mining</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">103</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks—Skype, LastFM and Google+—and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paolo Cintia</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Marco Malvaldi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The harsh rule of the goals: data-driven performance indicators for football teams</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE International Conference on Data Science and Advanced Analytics</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">—Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems’ view on football data has the potential of
revealing hidden patterns and behavior of superior quality.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Alina Sirbu</style></author><author><style face="normal" font="default" size="100%">Ozalp Babaoglu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q</style></title><secondary-title><style face="normal" font="default" size="100%">Euro-Par 2015: parallel Processing Workshops, LNCS 9523</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-319-27308-2_51</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer</style></publisher><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">José Antônio Fernandes de Macêdo</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Monica Wachowicz</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">How you move reveals who you are: understanding human behavior by analyzing trajectory data</style></title><secondary-title><style face="normal" font="default" size="100%">Knowl. Inf. Syst.</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/s10115-012-0511-z</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">37</style></volume><pages><style face="normal" font="default" size="100%">331–362</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Giulio Rossetti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">&quot;How Well Do We Know Each Other?&quot; Detecting Tie Strength in Multidimensional Social Networks</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Advances in Social Networks Analysis and Mining, {ASONAM} 2012, Istanbul, Turkey, 26-29 August 2012</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://doi.ieeecomputersociety.org/10.1109/ASONAM.2012.180</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Osman Abul</style></author><author><style face="normal" font="default" size="100%">Maurizio Atzori</style></author><author><style face="normal" font="default" size="100%">Francesco Bonchi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hiding Sensitive Trajectory Patterns</style></title><secondary-title><style face="normal" font="default" size="100%">ICDM Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">693-698</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Osman Abul</style></author><author><style face="normal" font="default" size="100%">Maurizio Atzori</style></author><author><style face="normal" font="default" size="100%">Francesco Bonchi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hiding Sequences</style></title><secondary-title><style face="normal" font="default" size="100%">SEBD</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">233-241</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Osman Abul</style></author><author><style face="normal" font="default" size="100%">Maurizio Atzori</style></author><author><style face="normal" font="default" size="100%">Francesco Bonchi</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Hiding Sequences</style></title><secondary-title><style face="normal" font="default" size="100%">ICDE Workshops</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year></dates><pages><style face="normal" font="default" size="100%">147-156</style></pages><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>