Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Camila Leite da Silva"'
Autor:
Luis Otavio Alvares, Vania Bogorny, Willian Zalewski, Carlos Andres Ferrero, Lucas May Petry, Camila Leite da Silva
Publikováno v:
Data Mining and Knowledge Discovery. 34:652-680
In the last few years trajectory classification has been applied to many real problems, basically considering the dimensions of space and time or attributes inferred from these dimensions. However, with the explosion of social media data and the adva
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d10892adaee539aff4856c0c8e85943
https://doi.org/10.1007/s10618-020-00676-x
https://doi.org/10.1007/s10618-020-00676-x
Autor:
Antonios Makris, José Antônio Fernandes de Macêdo, Konstantinos Tserpes, Vania Bogorny, Luis Otavio Alvares, Camila Leite da Silva
Publikováno v:
GeoInformatica
During the last few years the volumes of the data that synthesize trajectories have expanded to unparalleled quantities. This growth is challenging traditional trajectory analysis approaches and solutions are sought in other domains. In this work, we
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7efe2bf87155247f50b94892aac17337
https://zenodo.org/record/6338137
https://zenodo.org/record/6338137
Publikováno v:
Intelligent Systems ISBN: 9783030917012
Mobility data analysis has received significant attention in the last few years. Enriching spatial-temporal trajectory data with semantic information, which is the definition of Multiple Aspect Trajectories, presents lots of opportunities, but also m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d0ea434cf1d0d0f436f7de3d0c53eaf9
https://doi.org/10.1007/978-3-030-91702-9_31
https://doi.org/10.1007/978-3-030-91702-9_31
Autor:
Francisco Vicenzi, Camila Leite da Silva, Luis Otavio Alvares, Vania Bogorny, Lucas May Petry
Publikováno v:
SAC
In the last few years, several trajectory classification methods have been proposed for mobility data collected from GPS devices. Most of them only use information derived from the physical movement of the object, as speed, acceleration, and directio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::214d508c3c4aced8632c257f8f43ab6a
https://doi.org/10.1145/3341105.3374045
https://doi.org/10.1145/3341105.3374045
Publikováno v:
BRACIS
In this work, we propose an exploratory approach to analyze the categorization, spread and replication of news content in journalistic web portals. With an increasing number of possible sources, these analyses may aid on identifying reliable content
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1999c35c8cb94f0b25a6f79006646817
https://doi.org/10.1109/bracis.2019.00023
https://doi.org/10.1109/bracis.2019.00023
Publikováno v:
BRACIS
In trajectory classification tasks, the challenge is to find the subtrajectories or a set of features that better discriminate the class. However, there is a lack of robustness in existing methods, as they do not use the same datasets or do not compa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::608ab743ee93fecfe3b99e3001e4d891
https://doi.org/10.1109/bracis.2019.00141
https://doi.org/10.1109/bracis.2019.00141
Autor:
CAMILA LEITE DA SILVA
Publikováno v:
Repositório Institucional da PUC-RIO (Projeto Maxwell)
Pontifícia Universidade Católica do Rio de Janeiro (PUC-RIO)
instacron:PUC_RIO
Pontifícia Universidade Católica do Rio de Janeiro (PUC-RIO)
instacron:PUC_RIO
PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO PROGRAMA DE SUPORTE À PÓS-GRADUAÇÃO DE INSTS. DE ENSINO A present
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::be9e43aac1860d75833db7944dc9fbd6
https://doi.org/10.17771/pucrio.acad.27012
https://doi.org/10.17771/pucrio.acad.27012
Publikováno v:
International journal of geographical information science
34 (2020): 1428–1450. doi:10.1080/13658816.2019.1707835
info:cnr-pdr/source/autori:May Petry L.; Leite Da Silva C.; Esuli A.; Renso C.; Bogorny V./titolo:MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings/doi:10.1080%2F13658816.2019.1707835/rivista:International journal of geographical information science (Print)/anno:2020/pagina_da:1428/pagina_a:1450/intervallo_pagine:1428–1450/volume:34
International Journal of Geographical Information Science
34 (2020): 1428–1450. doi:10.1080/13658816.2019.1707835
info:cnr-pdr/source/autori:May Petry L.; Leite Da Silva C.; Esuli A.; Renso C.; Bogorny V./titolo:MARC: a robust method for multiple-aspect trajectory classification via space, time, and semantic embeddings/doi:10.1080%2F13658816.2019.1707835/rivista:International journal of geographical information science (Print)/anno:2020/pagina_da:1428/pagina_a:1450/intervallo_pagine:1428–1450/volume:34
International Journal of Geographical Information Science
The increasing popularity of Location-Based Social Networks (LBSNs) and the se- mantic enrichment of mobility data in several contexts in the last few years has led to the generation of large volumes of trajectory data. In contrast to GPS-based traje
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f98f085f88c336324299db2dcc920e6f