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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/12987
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dc.contributor.authorZibitsker, B.-
dc.date.accessioned2017-05-26T08:53:55Z-
dc.date.accessioned2017-07-18T11:53:10Z-
dc.date.available2017-05-26T08:53:55Z-
dc.date.available2017-07-18T11:53:10Z-
dc.date.issued2017-
dc.identifier.citationZibitsker, B. Incorporation of prescriptive analytics for performance engineering and dynamic performance management of Big Data applications / B. Zibitsker // BIG DATA and Advanced Analytics: collection of materials of the third international scientific and practical conference, Minsk, Belarus, May 3–4, 2017 / editorial board : М. Batura [et al.]. – Minsk : BSUIR, 2017. – С. 18.ru_RU
dc.identifier.urihttps://libeldoc.bsuir.by/handle/123456789/12987-
dc.description.abstractIn a complex Big Data environment applications compete for resources and affect each other performance. Selection of Machine Learning Algorithms and Machine Learning Libraries and Big Data YARN's Scheduler, Queues and Containers rules can significantly affect accuracy, performance and scalability of Big Data applications.ru_RU
dc.language.isoenru_RU
dc.publisherБГУИРru_RU
dc.subjectматериалы конференцийru_RU
dc.subjectBig Dataru_RU
dc.titleIncorporation of prescriptive analytics for performance engineering and dynamic performance management of Big Data applicationsru_RU
dc.typeArticleru_RU
Appears in Collections:BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2017)

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