01572nas a2200229   4500008004100000245004600041210004500087300001100132520090000143100001701043700002301060700002301083700002101106700001701127700002101144700002301165700002001188700001401208700002901222700001101251856008001262       2020                        eng d00aHuman migration: the big data perspective0 aHuman migration the big data perspective  a1–203 aHow 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.1 aSirbu, Alina1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aConti, Marco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aBertoli, Simone1 aKim, Jisu1 aMuntean, Cristina, Ioana1 aothers  uhttps://datasciencephd.eu/publications/human-migration-big-data-perspective01400nas a2200157   4500008004100000245004200041210004100083520091600124653002501040653001701065653001801082100002101100700002301121700001701144856008101161       2020                        eng d00aSelf-Adapting Trajectory Segmentation0 aSelfAdapting Trajectory Segmentation3 aIdentifying 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.10aMobility Data Mining10aSegmentation10aUser Modeling1 aBonavita, Agnese1 aGuidotti, Riccardo1 aNanni, Mirco  uhttps://datasciencephd.eu/publications/self-adapting-trajectory-segmentation01736nas a2200169   4500008004100000245007300041210006900114260001300183520117100196653003301367653001501400100002301415700002301438700001901461700002001480856006601500       2019                        eng d00aExplaining multi-label black-box classifiers for health applications0 aExplaining multilabel blackbox classifiers for health applicatio  bSpringer3 aToday 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.10aExplainable Machine Learning10aHealthcare1 aPanigutti, Cecilia1 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-24409-5_9