TY - JOUR T1 - Human migration: the big data perspective JF - International Journal of Data Science and Analytics Y1 - 2020 A1 - Alina Sirbu A1 - Andrienko, Gennady A1 - Andrienko, Natalia A1 - Boldrini, Chiara A1 - Conti, Marco A1 - Giannotti, Fosca A1 - Guidotti, Riccardo A1 - Bertoli, Simone A1 - Jisu Kim A1 - Muntean, Cristina Ioana A1 - others AB - 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. ER - TY - CONF T1 - Self-Adapting Trajectory Segmentation T2 - 3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020 Y1 - 2020 A1 - Agnese Bonavita A1 - Guidotti, Riccardo A1 - Nanni,Mirco KW - Mobility Data Mining KW - Segmentation KW - User Modeling AB - Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results. JF - 3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020 ER - TY - CONF T1 - Explaining multi-label black-box classifiers for health applications T2 - International Workshop on Health Intelligence Y1 - 2019 A1 - Cecilia Panigutti A1 - Guidotti, Riccardo A1 - Monreale, Anna A1 - Pedreschi, Dino KW - Explainable Machine Learning KW - Healthcare AB - Today the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length. JF - International Workshop on Health Intelligence PB - Springer UR - https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9 ER -