01400nas 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-segmentation00403nas a2200085   4500008004100000245008600041210006900127100002100196856010000217       2018                        eng d00aSearch for H-> mu mu in the VBF production channel with the CMS experiment at LHC0 aSearch for H mu mu in the VBF production channel with the CMS ex1 aBonavita, Agnese  uhttps://datasciencephd.eu/publications/search-h-mu-mu-vbf-production-channel-cms-experiment-lhc