000 | 03889nam a22006135i 4500 | ||
---|---|---|---|
001 | 978-3-319-61349-9 | ||
003 | DE-He213 | ||
005 | 20210201191345.0 | ||
007 | cr nn 008mamaa | ||
008 | 170820s2018 gw | s |||| 0|eng d | ||
020 |
_a9783319613499 _9978-3-319-61349-9 |
||
050 | 4 | _aQ342 | |
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 |