Zobrazeno 1 - 10
of 122
pro vyhledávání: '"Esther Pacitti"'
Publikováno v:
PeerJ Computer Science, Vol 7, p e606 (2021)
Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturi
Externí odkaz:
https://doaj.org/article/4a1af1f8b57d496490cdd7e7d7c35502
Autor:
Janio Lima, Rebecca Salles, Luciana Escobar, Cristiane Géa, Pedro Alpis Fernandes, Esther Pacitti, Fabio Porto, Rafaelli Coutinho, Eduardo Ogasawara
Publikováno v:
Anais Estendidos do XXXVII Simpósio Brasileiro de Banco de Dados (SBBD Estendido 2022).
Time series events detection relates to the study of techniques for detecting points in a series with special meaning which differs from the expected behavior of the dataset. In scenarios such as digital twins and IoT devices, there is natural genera
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::002fdc95872b7a887f9e1bdc2eca146d
https://doi.org/10.5753/sbbd_estendido.2022.21865
https://doi.org/10.5753/sbbd_estendido.2022.21865
Autor:
Janio Lima, Rebecca Salles, Fabio Porto, Rafaelli Coutinho, Pedro Alpis, Luciana Escobar, Esther Pacitti, Eduardo Ogasawara
Publikováno v:
IJCNN 2022-IEEE International Joint Conference on Neural Networks
IJCNN 2022-IEEE International Joint Conference on Neural Networks, IEEE, Jul 2022, Padova, Italy. pp.1-8, ⟨10.1109/IJCNN55064.2022.9892088⟩
IJCNN 2022-IEEE International Joint Conference on Neural Networks, IEEE, Jul 2022, Padova, Italy. pp.1-8, ⟨10.1109/IJCNN55064.2022.9892088⟩
International audience; Time series event detection is related to studying methods for detecting observations in a series with special meaning. These observations differ from the expected behavior of the data set. In data streaming scenarios, it is p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::258a6e326af30974c404063105677a51
https://doi.org/10.1109/ijcnn55064.2022.9892088
https://doi.org/10.1109/ijcnn55064.2022.9892088
Publikováno v:
Neurocomputing
Neurocomputing, Elsevier, 2022, 467, pp.197-202. ⟨10.1016/j.neucom.2021.09.067⟩
Neurocomputing, 2022, 467, pp.197-202. ⟨10.1016/j.neucom.2021.09.067⟩
Neurocomputing, Elsevier, 2022, 467, pp.197-202. ⟨10.1016/j.neucom.2021.09.067⟩
Neurocomputing, 2022, 467, pp.197-202. ⟨10.1016/j.neucom.2021.09.067⟩
International audience; The nonstationary time series prediction is challenging since it demands knowledge of both data transformation and prediction methods. This paper presents TSPred, a framework for nonstationary time series prediction. It differ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::266e3ba4667c86dfdb768fb6bc53805a
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452170/file/TSPred_preprint.pdf
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452170/file/TSPred_preprint.pdf
Autor:
Antonio Castro, Heraldo Borges, Ricardo Campisano, Esther Pacitti, Fabio Porto, Rafaelli Coutinho, Eduardo Ogasawara
Publikováno v:
SBBD 2021-Simpósio Brasileiro de Banco de Dados
SBBD 2021-Simpósio Brasileiro de Banco de Dados, SBC, Oct 2021, Online, Brazil. pp.313-318, ⟨10.5753/sbbd.2021.17891⟩
SBBD: Simpósio Brasileiro de Banco de Dados
SBBD: Simpósio Brasileiro de Banco de Dados, SBC, Oct 2021, Online, Brazil. pp.313-318
SBBD 2021-Simpósio Brasileiro de Banco de Dados, SBC, Oct 2021, Online, Brazil. pp.313-318, ⟨10.5753/sbbd.2021.17891⟩
SBBD: Simpósio Brasileiro de Banco de Dados
SBBD: Simpósio Brasileiro de Banco de Dados, SBC, Oct 2021, Online, Brazil. pp.313-318
International audience; Spatiotemporal patterns bring knowledge of sequences of events, place and time when they occur. Finding such patterns is a complex task and one of great value for different domains. However, not all patterns are frequent acros
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9848e46b2396a0232384b39bce99d687
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452154/document
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452154/document
Publikováno v:
Workshop on Computer and Communication Systems Performance (WPerformance 2021)
Workshop on Computer and Communication Systems Performance (WPerformance 2021), CSBC, Jul 2021, Online, Brazil. pp.12
WPerformance 2021-20th Workshop on Performance of Computer and Communication Systems
WPerformance 2021-20th Workshop on Performance of Computer and Communication Systems, CSBC, Jul 2021, Online, Brazil. pp.37-48, ⟨10.5753/wperformance.2021.15721⟩
Workshop on Computer and Communication Systems Performance (WPerformance 2021), CSBC, Jul 2021, Online, Brazil. pp.12
WPerformance 2021-20th Workshop on Performance of Computer and Communication Systems
WPerformance 2021-20th Workshop on Performance of Computer and Communication Systems, CSBC, Jul 2021, Online, Brazil. pp.37-48, ⟨10.5753/wperformance.2021.15721⟩
As nuvens fornecem um ambiente sob demanda que permite que cientistas migrem seus experimentos locais para um ambiente elástico. Os experimentos são modelados como workflows científicos, e muito deles são intensivos em computação e produção d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3c4429101b3ee073dc1eda95167b6cd4
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452298
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03452298
Publikováno v:
PeerJ Computer Science
PeerJ Computer Science, PeerJ, 2021, 7, pp.e606. ⟨10.7717/peerj-cs.606⟩
PeerJ Computer Science, 2021, 7, pp.e606. ⟨10.7717/peerj-cs.606⟩
PeerJ Computer Science, Vol 7, p e606 (2021)
PeerJ Computer Science, PeerJ, 2021, 7, pp.e606. ⟨10.7717/peerj-cs.606⟩
PeerJ Computer Science, 2021, 7, pp.e606. ⟨10.7717/peerj-cs.606⟩
PeerJ Computer Science, Vol 7, p e606 (2021)
Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bd5bd3c6e5ee6fa4c65a965cbd2fce0b
https://hal.archives-ouvertes.fr/hal-03418836/document
https://hal.archives-ouvertes.fr/hal-03418836/document
Publikováno v:
Synthesis Lectures on Data Management. 14:1-179
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b93237b87d24899960afd5f836e5b72
https://doi.org/10.2200/s00915ed1v01y201904dtm060
https://doi.org/10.2200/s00915ed1v01y201904dtm060
Autor:
Boyan Kolev, Marta Patiño-Martínez, Pavlos Kranas, Esther Pacitti, Patrick Valduriez, Oleksandra Levchenko, Ricardo Jiménez-Peris
Publikováno v:
Distributed and Parallel Databases
Distributed and Parallel Databases, Springer, In press, pp.39. ⟨10.1007/s10619-021-07322-5⟩
Distributed and Parallel Databases, 2021, 39, pp.939-977. ⟨10.1007/s10619-021-07322-5⟩
Distributed and Parallel Databases, Springer, In press, pp.39. ⟨10.1007/s10619-021-07322-5⟩
Distributed and Parallel Databases, 2021, 39, pp.939-977. ⟨10.1007/s10619-021-07322-5⟩
International audience; The blooming of different data stores has made polystores a major topic in the cloud and big data landscape. As the amount of data grows rapidly, it becomes critical to exploit the inherent parallel processing capabilities of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::08bd82cb810fb609c44599f522b6dcdd
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03148271/file/PMSQP_v4.4.pdf
https://hal-lirmm.ccsd.cnrs.fr/lirmm-03148271/file/PMSQP_v4.4.pdf
Autor:
Esther Pacitti, François Tardieu, Christophe Pradal, Gaëtan Heidsieck, Patrick Valduriez, Daniel de Oliveira
Publikováno v:
Future Generation Computer Systems
Future Generation Computer Systems, 2021, 122, pp.172-186. ⟨10.1016/j.future.2021.03.012⟩
Future Generation Computer Systems, Elsevier, 2021, 122, pp.172-186. ⟨10.1016/j.future.2021.03.012⟩
Future Generation Computer Systems, 2021, 122, pp.172-186. ⟨10.1016/j.future.2021.03.012⟩
Future Generation Computer Systems, Elsevier, 2021, 122, pp.172-186. ⟨10.1016/j.future.2021.03.012⟩
International audience; Many scientific experiments today are performed using scientific workflows, which become more and more data-intensive. We consider the efficient execution of such workflows in a multisite cloud, leveraging heterogeneous resour
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77316185cf193aace1fa2f924b72aba2
https://hal.science/hal-03189130/document
https://hal.science/hal-03189130/document