Chiara Renso
Chiara Renso is a researcher at the ISTI Institute of CNR, the largest CNR institute involved in ICT Research. She has been ``capo commessa'' of CNR for the ``commessa'' KDD from 2008 to 2014. Her main research interests are in the area of mobility data mining, machine learning for mobility data, semantic enrichment of trajectory data. The focus is addressing interesting, often yet unsolved problems, related to the enrich, represent, analyse semantic rich trajectory data for several applications fields including specifically tourism and traffic management. He has published more than 100 papers on these topics on peer-reviewed journals, books, and proceedings of international workshops/conferences. He participated into several activities in several European and national projects. She was the project coordinator of a FP7-MarieCurie-IRSES project named SEEK and the coordinator of an international bilateral project CNR-CNPQ (Brazil) from 2012 to 2014. She is currently the project coordinator of the H2020-MSCA-RISE project called MASTER on multiple aspects trajectories management and analysis. She is expert evaluator for the H2020 program of the European Commission.Chiara Renso is a researcher at the ISTI Institute of CNR, the largest CNR institute involved in ICT Research. She has been ``capo commessa'' of CNR for the ``commessa'' KDD from 2008 to 2014. Her main research interests are in the area of mobility data mining, machine learning for mobility data, semantic enrichment of trajectory data. The focus is addressing interesting, often yet unsolved problems, related to the enrich, represent, analyse semantic rich trajectory data for several applications fields including specifically tourism and traffic management. He has published more than 100 papers on these topics on peer-reviewed journals, books, and proceedings of international workshops/conferences. He participated into several activities in several European and national projects. She was the project coordinator of a FP7-MarieCurie-IRSES project named SEEK and the coordinator of an international bilateral project CNR-CNPQ (Brazil) from 2012 to 2014. She is currently the project coordinator of the H2020-MSCA-RISE project called MASTER on multiple aspects trajectories management and analysis. She is expert evaluator for the H2020 program of the European Commission.
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About Chiara Renso
Chiara Renso is a researcher at the ISTI Institute of CNR, the largest CNR institute involved in ICT Research. She has been ``capo commessa'' of CNR for the ``commessa'' KDD from 2008 to 2014. Her main research interests are in the area of mobility data mining, machine learning for mobility data, semantic enrichment of trajectory data. The focus is addressing interesting, often yet unsolved problems, related to the enrich, represent, analyse semantic rich trajectory data for several applications fields including specifically tourism and traffic management. He has published more than 100 papers on these topics on peer-reviewed journals, books, and proceedings of international workshops/conferences. He participated into several activities in several European and national projects. She was the project coordinator of a FP7-MarieCurie-IRSES project named SEEK and the coordinator of an international bilateral project CNR-CNPQ (Brazil) from 2012 to 2014. She is currently the project coordinator of the H2020-MSCA-RISE project called MASTER on multiple aspects trajectories management and analysis. She is expert evaluator for the H2020 program of the European Commission.
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Multiple-Aspect Analysis of Semantic Trajectories :First International Workshop, MASTER 2019, Held in Conjunction with ECML-PKDD 2019, Würzburg, Germany, September 16, 2019, Proceedings
This open access book constitutes the refereed post-conference proceedings of the First International Workshop on Multiple-Aspect Analysis of Semantic Trajectories, MASTER 2019, held in conjunction with the 19th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, in Würzburg, Germany, in September 2019. The 8 full papers presented were carefully reviewed and selected from 12 submissions. They represent an interesting mix of techniques to solve recurrent as well as new problems in the semantic trajectory domain, such as data representation models, data management systems, machine learning approaches for anomaly detection, and common pathways identification.
