Developments in Web and Wireless technologies have enabled the diffusion of large volumes of geospatial mobility data, and new challenges and opportunities have emerged for the GIScience research community, interested in extracting knowledge from these data. In most data analytics scenarios, well-known analytics platforms, such as KNIME or RapidMiner, offer practical general-purpose tools to data analysts. However, when dealing with mobility data, these platforms provide only limited support to some peculiar geospatial data manipulation tasks, thus forcing researchers and practitioners to manually implement significant portions of their pipelines, hindering productivity and replicability of the results. This paper presents a solution we are currently working on to support mobility data analysis. Our prototype, which we called KNOT (KNime mObility Toolkit), extends the KNIME Analytics Platform with a collection of new components specifically designed to support processing steps typical of mobility data, including map-matching, trajectory partitioning, and road network coverage analysis. To show the effectiveness of these components, we report also on how we applied them to perform a realistic analytical task on a real-world massive mobility dataset.
Mobility Data Analytics with KNOT: The KNime mObility Toolkit / DI MARTINO, Sergio; Mazzocca, N.; ROCCO DI TORREPADULA, Franca; Starace, LUIGI LIBERO LUCIO. - 13912:(2023), pp. 95-104. (Intervento presentato al convegno 20th International Symposium on Web and Wireless Geographical Information Systems, W2GIS 2023 tenutosi a Quebec, Canada nel 2023) [10.1007/978-3-031-34612-5_6].
Mobility Data Analytics with KNOT: The KNime mObility Toolkit
Di Martino Sergio;Mazzocca N.;Rocco Di Torrepadula Franca;Starace Luigi Libero Lucio
2023
Abstract
Developments in Web and Wireless technologies have enabled the diffusion of large volumes of geospatial mobility data, and new challenges and opportunities have emerged for the GIScience research community, interested in extracting knowledge from these data. In most data analytics scenarios, well-known analytics platforms, such as KNIME or RapidMiner, offer practical general-purpose tools to data analysts. However, when dealing with mobility data, these platforms provide only limited support to some peculiar geospatial data manipulation tasks, thus forcing researchers and practitioners to manually implement significant portions of their pipelines, hindering productivity and replicability of the results. This paper presents a solution we are currently working on to support mobility data analysis. Our prototype, which we called KNOT (KNime mObility Toolkit), extends the KNIME Analytics Platform with a collection of new components specifically designed to support processing steps typical of mobility data, including map-matching, trajectory partitioning, and road network coverage analysis. To show the effectiveness of these components, we report also on how we applied them to perform a realistic analytical task on a real-world massive mobility dataset.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
