Semantic Enrichment of Mobility Data: A Comprehensive Methodology and the MAT-BUILDER System

Autor: Francesco Lettich, Chiara Pugliese, Chiara Renso, Fabio Pinelli

Publikováno v: IEEE Access, Vol 11, Pp 90857-90875 (2023)

The widespread adoption of personal location devices, the Internet of Mobile Things, and Location Based Social Networks, enables the collection of vast amounts of movement data. This data often needs to be enriched with a variety of semantic dimensio

Externí odkaz: https://doaj.org/article/71a19e26867141c1b366db953f77e3d0

Cloud and data federation in MobiDataLab

Autor: Francesco Lettich, Patrizio Dazzi, Emanuele Carlini, Raffaele Perego, Chiara Renso

Publikováno v: FRAME'21-1st Workshop on Flexible Resource and Application Management on the Edge, pp. 39–40, Virtual Event, Sweden, 25/06/2021
FRAME '21: Proceedings of the 1st Workshop on Flexible Resource and Application Management on the Edge 2021
FRAME'21-1st Workshop on Flexible Resource and Application Management on the Edge, Virtual Event, Sweden, 25/06/2021
info:cnr-pdr/source/autori:Carlini E.; Dazzi P.; Lettich F.; Perego R.; Renso C./congresso_nome:FRAME'21-1st Workshop on Flexible Resource and Application Management on the Edge/congresso_luogo:Virtual Event, Sweden/congresso_data:25%2F06%2F2021/anno:2021/pagina_da:/pagina_a:/intervallo_pagine
Proceedings of the 1st Workshop on Flexible Resource and Application Management on the Edge

Today's innovative digital services dealing with the mobility of per- sons and goods produce huge amount of data. To propose advanced and efficient mobility services, the collection and aggregation of new sources of data from various producers are ne

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5e31a8a85a5b6f4f33e1672979631379
https://openportal.isti.cnr.it/doc?id=people______::86b242454e6837364c1618b55525a845

Online Clustering of Trajectories in Road Networks

Autor: José Antônio Fernandes de Macêdo, Ticiana L. Coelho da Silva, Karine Zeitouni, Francesco Lettich, Marco A. Casanova

Publikováno v: MDM

The ubiquity of GPS-enabled smartphones and automotive navigation systems allows to monitor and collect massive streams of trajectory data in real-time. This enables real-time analyses on mobility data in urban settings, which in turn have the potent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6fd4e8048a06206c7c8d19fb8e8a73e3
https://doi.org/10.1109/mdm48529.2020.00031

Leveraging feature selection to detect potential tax fraudsters

Autor: Tales Matos, Franco Maria Nardini, Francesco Lettich, José Maria Monteiro, Raffaele Perego, Chiara Renso, José Antônio Fernandes de Macêdo

Publikováno v: Expert systems with applications 145 (2020). doi:10.1016/j.eswa.2019.113128
info:cnr-pdr/source/autori:Matos T.; Macedo J.A.; Lettich F.; Monteiro J.M.; Renso C.; Perego R.; Nardini F.M./titolo:Leveraging feature selection to detect potential tax fraudsters/doi:10.1016%2Fj.eswa.2019.113128/rivista:Expert systems with applications/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:/volume:145

Tax evasion is any act that knowingly or unknowingly, legally or unlawfully, leads to non-payment or underpayment of tax due. Enforcing the correct payment of taxes by taxpayers is fundamental in maintaining investments that are necessary and benefit

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5c9cf15ee452beeb1dab0ffda3480d77
https://openportal.isti.cnr.it/doc?id=people______::c709d5f80e71c2c217f0f5f10ddd20fd

Parallel Traversal of Large Ensembles of Decision Trees

Autor: Franco Maria Nardini, Claudio Lucchese, Salvatore Orlando, Raffaele Perego, Rossano Venturini, Nicola Tonellotto, Francesco Lettich

Publikováno v: IEEE Transactions on Parallel and Distributed Systems
IEEE transactions on parallel and distributed systems
30 (2019): 2075–2089. doi:10.1109/TPDS.2018.2860982
info:cnr-pdr/source/autori:Lettich F.; Lucchese C.; Nardini F.M.; Orlando S.; Perego R.; Tonellotto N.; Venturini R./titolo:Parallel Traversal of Large Ensembles of Decision Trees/doi:10.1109%2FTPDS.2018.2860982/rivista:IEEE transactions on parallel and distributed systems (Print)/anno:2019/pagina_da:2075/pagina_a:2089/intervallo_pagine:2075–2089/volume:30

Machine-learnt models based on additive ensembles of regression trees are currently deemed the best solution to address complex classification, regression, and ranking tasks. The deployment of such models is computationally demanding: to compute the

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57b75b0fcc417f09483e87e39882e60f
http://hdl.handle.net/10278/3703670