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Scientific Data Analysis Using Data-Intensive Scalable Computing: the SciDISC Project

Autor: Valduriez, P., Mattoso, M., Akbarinia, R., Borges, H., Camata, J., Coutinho, A., Gaspar, D., Lemus, N., Liu, J., Lustosa, H., Masseglia, F., Silva, F. N. D., Silva, V., Souza, R., Ocaña, K., Eduardo Ogasawara, Oliveira, D. D., Pacitti, E., Porto, F., Shasha, D.

Publikováno v: Proceedings of the Latin America Data Science Workshop co-located with 44th International Conference on Very Large Data Bases (VLDB)
LADaS: Latin America Data Science Workshop
LADaS: Latin America Data Science Workshop, Aug 2018, Rio de Janeiro, Brazil
BASE-Bielefeld Academic Search Engine
Scopus-Elsevier
Kary Ocaña

International audience; Data-intensive science requires the integration of two fairly different paradigms: high-performance computing (HPC) and data-intensive scalable computing (DISC), as exemplified by frameworks such as Hadoop and Spark. In this c

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::2dc2ffde65b313bdf1c256cd53358b08
https://hal-lirmm.ccsd.cnrs.fr/lirmm-01867804/document

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ABS: The Anti Bouncing Model for Usage Data Streams.

Autor: Chongsheng Zhang, Masseglia, F., Lechevallier, Y.

Publikováno v: 2010 IEEE 10th International Conference on Data Mining (ICDM); 2010, p1169-1174, 6p

TED and EVA: Expressing temporal tendencies among quantitative variables using fuzzy sequential patterns.

Autor: Fiot, C., Masseglia, F., Laurent, A., Teisseire, M.

Publikováno v: 2008 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence); 2008, p1861-1868, 8p