FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2904217883> ?p ?o ?g. }

• W2904217883 endingPage "286" @default.
• W2904217883 startingPage "271" @default.
• W2904217883 abstract "In modern digital world, we have data deluge, but still starving for information. Big Data era is characterized by vast amounts of data sized in the order of petabytes or even exabytes coming at high speed from variety of sources. These unstructured data have got tremendous potential, but Big data by itself has no value unless it is processed leading to derivation of meaningful insights. This is where Machine Learning comes into picture which helps machine to learn and act on its own. Machine Learning can help us to sniff through enormous quantities of data, process them and get meaningful results. The confluence of Big Data and Machine Learning is allowing organizations to automate and improve complex descriptive, predictive and prescriptive analytical tasks and arriving at informed decision making. This is to say that, harnessing the value & power of Big Data can offer great insights to the companies with the help of Machine Learning (ML) increasing their revenues and providing a competitive advantage over their rivals. Machine Learning is acting as a catalyst to derive tangible value from Big Data and serving as key to unlocking the potential of Big Data Analytic. The management of big data gives rise to concerns regarding data collection efficiency, data processing, analytic, and security thereby opening new paradigms of research & innovations. This is a hot research area and amalgamation of Machine Learning with Big Data is proving to be major performance booster providing information which were hidden and not to be seen earlier. ML based algorithms and development in the area are explored and discussed at length in this chapter. It focuses on applications of Machine Learning to Big Data, issues, challenges and most recent trends in the area." @default.
• W2904217883 created "2018-12-22" @default.
• W2904217883 creator A5059313873 @default.
• W2904217883 date "2018-12-08" @default.
• W2904217883 modified "2023-09-25" @default.
• W2904217883 title "Unleashing Machine Learning onto Big Data: Issues, Challenges and Trends" @default.
• W2904217883 cites W1124052577 @default.
• W2904217883 cites W165700604 @default.
• W2904217883 cites W1901616594 @default.
• W2904217883 cites W1937348540 @default.
• W2904217883 cites W1971647276 @default.
• W2904217883 cites W1983507674 @default.
• W2904217883 cites W1984670829 @default.
• W2904217883 cites W1989496436 @default.
• W2904217883 cites W1990361999 @default.
• W2904217883 cites W1990488619 @default.
• W2904217883 cites W2004913172 @default.
• W2904217883 cites W2005697477 @default.
• W2904217883 cites W2018992486 @default.
• W2904217883 cites W2031436300 @default.
• W2904217883 cites W2037307005 @default.
• W2904217883 cites W2070560815 @default.
• W2904217883 cites W2073539200 @default.
• W2904217883 cites W2074685362 @default.
• W2904217883 cites W2075754841 @default.
• W2904217883 cites W2089468765 @default.
• W2904217883 cites W2118023920 @default.
• W2904217883 cites W2125816831 @default.
• W2904217883 cites W2127282486 @default.
• W2904217883 cites W2134254435 @default.
• W2904217883 cites W2141975087 @default.
• W2904217883 cites W2163922914 @default.
• W2904217883 cites W2173440868 @default.
• W2904217883 cites W2261525379 @default.
• W2904217883 cites W2406349003 @default.
• W2904217883 cites W2524620548 @default.
• W2904217883 cites W2534219692 @default.
• W2904217883 cites W2576683119 @default.
• W2904217883 cites W2579247884 @default.
• W2904217883 cites W2726150830 @default.
• W2904217883 doi "https://doi.org/10.1007/978-3-030-02357-7_13" @default.
• W2904217883 hasPublicationYear "2018" @default.
• W2904217883 type Work @default.
• W2904217883 sameAs 2904217883 @default.
• W2904217883 citedByCount "1" @default.
• W2904217883 countsByYear W29042178832022 @default.
• W2904217883 crossrefType "book-chapter" @default.
• W2904217883 hasAuthorship W2904217883A5059313873 @default.
• W2904217883 hasConcept C119857082 @default.
• W2904217883 hasConcept C124101348 @default.
• W2904217883 hasConcept C154945302 @default.
• W2904217883 hasConcept C2522767166 @default.
• W2904217883 hasConcept C41008148 @default.
• W2904217883 hasConcept C75684735 @default.
• W2904217883 hasConceptScore W2904217883C119857082 @default.
• W2904217883 hasConceptScore W2904217883C124101348 @default.
• W2904217883 hasConceptScore W2904217883C154945302 @default.
• W2904217883 hasConceptScore W2904217883C2522767166 @default.
• W2904217883 hasConceptScore W2904217883C41008148 @default.
• W2904217883 hasConceptScore W2904217883C75684735 @default.
• W2904217883 hasLocation W29042178831 @default.
• W2904217883 hasOpenAccess W2904217883 @default.
• W2904217883 hasPrimaryLocation W29042178831 @default.
• W2904217883 hasRelatedWork W2397053934 @default.
• W2904217883 hasRelatedWork W2617449561 @default.
• W2904217883 hasRelatedWork W2808989540 @default.
• W2904217883 hasRelatedWork W2961085424 @default.
• W2904217883 hasRelatedWork W3014300295 @default.
• W2904217883 hasRelatedWork W3158877728 @default.
• W2904217883 hasRelatedWork W3189515467 @default.
• W2904217883 hasRelatedWork W4224943336 @default.
• W2904217883 hasRelatedWork W4293567684 @default.
• W2904217883 hasRelatedWork W4322629366 @default.
• W2904217883 isParatext "false" @default.
• W2904217883 isRetracted "false" @default.
• W2904217883 magId "2904217883" @default.
• W2904217883 workType "book-chapter" @default.