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008 170820s2018 gw | s |||| 0|eng d
020 _a9783319613499
_9978-3-319-61349-9
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072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aLerma, L. Octavio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aTowards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data
_h[electronic resource] /
_cby L. Octavio Lerma, Vladik Kreinovich.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aVIII, 141 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Big Data,
_x2197-6503 ;
_v29
500 _aAcceso multiusuario
505 0 _aIntroduction --  Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing --  Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions.
520 _aThis book describes analytical techniques for optimizing knowledge acquisition, processing, and propagation, especially in the contexts of cyber-infrastructure and big data. Further, it presents easy-to-use analytical models of knowledge-related processes and their applications. The need for such methods stems from the fact that, when we have to decide where to place sensors, or which algorithm to use for processing the data-we mostly rely on experts' opinions. As a result, the selected knowledge-related methods are often far from ideal. To make better selections, it is necessary to first create easy-to-use models of knowledge-related processes. This is especially important for big data, where traditional numerical methods are unsuitable. The book offers a valuable guide for everyone interested in big data applications: students looking for an overview of related analytical techniques, practitioners interested in applying optimization techniques, and researchers seeking to improve and expand on these techniques.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aComputational intelligence.
650 0 _aData mining.
650 0 _aBig data.
650 0 _aArtificial intelligence.
650 1 4 _aComputational Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/T11014
650 2 4 _aData Mining and Knowledge Discovery.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I18030
650 2 4 _aBig Data.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I29120
650 2 4 _aBig Data/Analytics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/522070
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
700 1 _aKreinovich, Vladik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319613482
776 0 8 _iPrinted edition:
_z9783319613505
776 0 8 _iPrinted edition:
_z9783319870588
830 0 _aStudies in Big Data,
_x2197-6503 ;
_v29
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-3-319-61349-9
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cLIBRO_ELEC
999 _c242533
_d242532