FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2748864189> ?p ?o ?g. }

• W2748864189 endingPage "64" @default.
• W2748864189 startingPage "54" @default.
• W2748864189 abstract "In the last years, the volume of information is growing faster than ever before, moving from small to huge, structured to unstructured datasets like text, image, audio and video. The purpose of processing the data is aimed to extract relevant information on trends, challenges and opportunities; all these studies with large volumes of data. The increase in the power of parallel computing enabled the use of Machine Learning (ML) techniques to take advantage of the processing capabilities offered by new architectures on large volumes of data. For this reason, it is necessary to find mechanisms that allow classify and organize them to facilitate to the users the extraction of the required information. The processing of these data requires the use of classification techniques that will be reviewed. This work analyzes different studies carried out on the use of ML for processing large volumes of data (Big Multimedia Data) and proposes a classification, using as criteria, the hardware infrastructures used in works of machine learning parallel approaches applied to large volumes of data." @default.
• W2748864189 created "2017-08-31" @default.
• W2748864189 creator A5067807571 @default.
• W2748864189 creator A5077785088 @default.
• W2748864189 creator A5079599826 @default.
• W2748864189 date "2017-07-01" @default.
• W2748864189 modified "2023-09-26" @default.
• W2748864189 title "A Review of Infrastructures to Process Big Multimedia Data" @default.
• W2748864189 cites W1036050411 @default.
• W2748864189 cites W1158935686 @default.
• W2748864189 cites W1429921896 @default.
• W2748864189 cites W1491165167 @default.
• W2748864189 cites W155760863 @default.
• W2748864189 cites W1851422430 @default.
• W2748864189 cites W1959178355 @default.
• W2748864189 cites W1963624607 @default.
• W2748864189 cites W1975291854 @default.
• W2748864189 cites W2038412523 @default.
• W2748864189 cites W2045275492 @default.
• W2748864189 cites W2072750586 @default.
• W2748864189 cites W2096118443 @default.
• W2748864189 cites W2121456247 @default.
• W2748864189 cites W2126623642 @default.
• W2748864189 cites W2145607950 @default.
• W2748864189 cites W2164364358 @default.
• W2748864189 cites W2261525379 @default.
• W2748864189 cites W2293238400 @default.
• W2748864189 cites W2306386573 @default.
• W2748864189 cites W2333002587 @default.
• W2748864189 cites W2337976471 @default.
• W2748864189 cites W2438467919 @default.
• W2748864189 cites W2518179444 @default.
• W2748864189 cites W2530314904 @default.
• W2748864189 cites W2537316881 @default.
• W2748864189 cites W271647429 @default.
• W2748864189 cites W280853923 @default.
• W2748864189 cites W316408160 @default.
• W2748864189 cites W4238584892 @default.
• W2748864189 cites W841229804 @default.
• W2748864189 doi "https://doi.org/10.4018/ijcvip.2017070105" @default.
• W2748864189 hasPublicationYear "2017" @default.
• W2748864189 type Work @default.
• W2748864189 sameAs 2748864189 @default.
• W2748864189 citedByCount "3" @default.
• W2748864189 countsByYear W27488641892020 @default.
• W2748864189 countsByYear W27488641892021 @default.
• W2748864189 countsByYear W27488641892022 @default.
• W2748864189 crossrefType "journal-article" @default.
• W2748864189 hasAuthorship W2748864189A5067807571 @default.
• W2748864189 hasAuthorship W2748864189A5077785088 @default.
• W2748864189 hasAuthorship W2748864189A5079599826 @default.
• W2748864189 hasBestOaLocation W27488641892 @default.
• W2748864189 hasConcept C111919701 @default.
• W2748864189 hasConcept C121332964 @default.
• W2748864189 hasConcept C124101348 @default.
• W2748864189 hasConcept C138827492 @default.
• W2748864189 hasConcept C17744445 @default.
• W2748864189 hasConcept C195807954 @default.
• W2748864189 hasConcept C199539241 @default.
• W2748864189 hasConcept C20556612 @default.
• W2748864189 hasConcept C23123220 @default.
• W2748864189 hasConcept C2522767166 @default.
• W2748864189 hasConcept C2777466982 @default.
• W2748864189 hasConcept C2779473830 @default.
• W2748864189 hasConcept C2781252014 @default.
• W2748864189 hasConcept C41008148 @default.
• W2748864189 hasConcept C49774154 @default.
• W2748864189 hasConcept C62520636 @default.
• W2748864189 hasConcept C75684735 @default.
• W2748864189 hasConcept C77088390 @default.
• W2748864189 hasConcept C98045186 @default.
• W2748864189 hasConceptScore W2748864189C111919701 @default.
• W2748864189 hasConceptScore W2748864189C121332964 @default.
• W2748864189 hasConceptScore W2748864189C124101348 @default.
• W2748864189 hasConceptScore W2748864189C138827492 @default.
• W2748864189 hasConceptScore W2748864189C17744445 @default.
• W2748864189 hasConceptScore W2748864189C195807954 @default.
• W2748864189 hasConceptScore W2748864189C199539241 @default.
• W2748864189 hasConceptScore W2748864189C20556612 @default.
• W2748864189 hasConceptScore W2748864189C23123220 @default.
• W2748864189 hasConceptScore W2748864189C2522767166 @default.
• W2748864189 hasConceptScore W2748864189C2777466982 @default.
• W2748864189 hasConceptScore W2748864189C2779473830 @default.
• W2748864189 hasConceptScore W2748864189C2781252014 @default.
• W2748864189 hasConceptScore W2748864189C41008148 @default.
• W2748864189 hasConceptScore W2748864189C49774154 @default.
• W2748864189 hasConceptScore W2748864189C62520636 @default.
• W2748864189 hasConceptScore W2748864189C75684735 @default.
• W2748864189 hasConceptScore W2748864189C77088390 @default.
• W2748864189 hasConceptScore W2748864189C98045186 @default.
• W2748864189 hasIssue "3" @default.
• W2748864189 hasLocation W27488641891 @default.
• W2748864189 hasLocation W27488641892 @default.
• W2748864189 hasOpenAccess W2748864189 @default.
• W2748864189 hasPrimaryLocation W27488641891 @default.
• W2748864189 hasRelatedWork W1819577652 @default.
• W2748864189 hasRelatedWork W2308923114 @default.
• W2748864189 hasRelatedWork W2322171752 @default.

• next