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pro vyhledávání: '"Porto, Fábio"'
Predictive queries over spatiotemporal (ST) stream data pose significant data processing and analysis challenges. ST data streams involve a set of time series whose data distributions may vary in space and time, exhibiting multiple distinct patterns.
Externí odkaz:
http://arxiv.org/abs/2410.00933
Autor:
Salles, Rebecca, Lima, Janio, Reis, Michel, Coutinho, Rafaelli, Pacitti, Esther, Masseglia, Florent, Akbarinia, Reza, Chen, Chao, Garibaldi, Jonathan, Porto, Fabio, Ogasawara, Eduardo
Publikováno v:
Computers & Industrial Engineering, Volume 198, 2024, 110728,ISSN 0360-8352
Time series event detection methods are evaluated mainly by standard classification metrics that focus solely on detection accuracy. However, inaccuracy in detecting an event can often result from its preceding or delayed effects reflected in neighbo
Externí odkaz:
http://arxiv.org/abs/2304.00439
Autor:
Salles, Rebecca, Lima, Janio, Reis, Michel, Coutinho, Rafaelli, Pacitti, Esther, Masseglia, Florent, Akbarinia, Reza, Chen, Chao, Garibaldi, Jonathan, Porto, Fabio, Ogasawara, Eduardo
Publikováno v:
In Computers & Industrial Engineering December 2024 198
Autor:
Abrahão, Felipe S., Zenil, Hector, Porto, Fabio, Winter, Michael, Wehmuth, Klaus, D'Ottaviano, Itala M. L.
When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processe
Externí odkaz:
http://arxiv.org/abs/2112.12275
Deep learning models have become increasingly useful in many different industries. On the domain of image classification, convolutional neural networks proved the ability to learn robust features for the closed set problem, as shown in many different
Externí odkaz:
http://arxiv.org/abs/2102.03243
Autor:
Paixão, Balthazar, Baroni, Lais, Salles, Rebecca, Escobar, Luciana, de Sousa, Carlos, Pedroso, Marcel, Saldanha, Raphael, Coutinho, Rafaelli, Porto, Fabio, Ogasawara, Eduardo
Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to controlling, there are still few studies focused on under-reporting estimates. It is believed that under-reporti
Externí odkaz:
http://arxiv.org/abs/2006.12759
Autor:
Souto, Yania Molina, Pereira, Rafael, Zorrilla, Rocío, Chaves, Anderson, Tsan, Brian, Rusu, Florin, Ogasawara, Eduardo, Ziviani, Artur, Porto, Fabio
In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries. Our approach is divided into two parts -- offline and online. Duri
Externí odkaz:
http://arxiv.org/abs/2005.11093
Akademický článek
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Applying machine learning models to meteorological data brings many opportunities to the Geosciences field, such as predicting future weather conditions more accurately. In recent years, modeling meteorological data with deep neural networks has beco
Externí odkaz:
http://arxiv.org/abs/1912.00134
Autor:
Lustosa, Hermano, Porto, Fabio
Scientific applications produce a huge amount of data, which imposes serious management and analysis challenges. In particular, limitations in current database management systems prevent their adoption in simulation applications, in which in-situ ana
Externí odkaz:
http://arxiv.org/abs/1903.02949