Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data
This 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 me...
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| Hlavní autor: | |
|---|---|
| Médium: | Elektronický zdroj E-kniha |
| Jazyk: | angličtina |
| Vydáno: |
Cham :
Springer International Publishing,
2018.
|
| Vydání: | 1st ed. 2018. |
| Edice: | Studies in Big Data,
29 |
| Témata: | |
| ISBN: | 9783319613499 |
| ISSN: | 2197-6503 ; |
| On-line přístup: |
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| 008 | 170820s2018 gw | s |||| 0|eng d | ||
| 020 | |a 9783319613499 | ||
| 024 | 7 | |a 10.1007/978-3-319-61349-9 |2 doi | |
| 035 | |a CVTIDW14823 | ||
| 040 | |a Springer-Nature |b eng |c CVTISR |e AACR2 | ||
| 041 | |a eng | ||
| 100 | 1 | |a Lerma, L. Octavio. |4 aut | |
| 245 | 1 | 0 | |a Towards Analytical Techniques for Optimizing Knowledge Acquisition, Processing, Propagation, and Use in Cyberinfrastructure and Big Data |h [electronic resource] / |c by L. Octavio Lerma, Vladik Kreinovich. |
| 250 | |a 1st ed. 2018. | ||
| 260 | 1 | |a Cham : |b Springer International Publishing, |c 2018. | |
| 300 | |a VIII, 141 p. |b online resource. | ||
| 490 | 1 | |a Studies in Big Data, |x 2197-6503 ; |v 29 | |
| 500 | |a Engineering | ||
| 505 | 0 | |a Introduction -- Data Acquisition: Towards Optimal Use of Sensors -- Data and Knowledge Processing -- Knowledge Propagation and Resulting Knowledge Enhancement -- Knowledge Use -- Conclusions. | |
| 516 | |a text file PDF | ||
| 520 | |a This 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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Data mining. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Artificial intelligence. | |
| 856 | 4 | 0 | |u http://hanproxy.cvtisr.sk/han/cvti-ebook-springer-eisbn-978-3-319-61349-9 |y Vzdialený prístup pre registrovaných používateľov |
| 910 | |b ZE12103 | ||
| 919 | |a 978-3-319-61349-9 | ||
| 974 | |a andrea.lebedova |f Elektronické zdroje | ||
| 992 | |a SUD | ||
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