000 | 03093nam a22005415i 4500 | ||
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001 | 978-3-319-27520-8 | ||
003 | DE-He213 | ||
005 | 20200421112221.0 | ||
007 | cr nn 008mamaa | ||
008 | 160205s2016 gw | s |||| 0|eng d | ||
020 |
_a9783319275208 _9978-3-319-27520-8 |
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024 | 7 |
_a10.1007/978-3-319-27520-8 _2doi |
|
050 | 4 | _aQ342 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
082 | 0 | 4 |
_a006.3 _223 |
245 | 1 | 0 |
_aTechniques and Environments for Big Data Analysis _h[electronic resource] : _bParallel, Cloud, and Grid Computing / _cedited by Bhabani Shankar Prasad Mishra, Satchidananda Dehuri, Euiwhan Kim, Gi-Name Wang. |
250 | _a1st ed. 2016. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016. |
|
300 |
_aXI, 191 p. 103 illus., 27 illus. in color. _bonline resource. |
||
336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
||
490 | 1 |
_aStudies in Big Data, _x2197-6503 ; _v17 |
|
505 | 0 | _aIntroduction to Big Data Analysis -- Parallel Environments -- A Deep Dive into the Hadoop World to Explore its Various Performances -- Natural Language Processing and Machine Learning for Big Data -- Big Data and Cyber Foraging: Future Scope and Challenges -- Parallel GA in Big Data Analysis -- Evolutionary Algorithm Based Techniques to Handle Big Data -- Statistical and Evolutionary Feature Selection Techniques Parallelized using MapReduce Programming Model -- A Data Aware Scheme for Scheduling Big-Data Applications on SAVANNA Hadoop -- The Role of Grid Technologies: A Next Level Combat with Big Data. | |
520 | _aThis volume is aiming at a wide range of readers and researchers in the area of Big Data by presenting the recent advances in the fields of Big Data Analysis, as well as the techniques and tools used to analyze it. The book includes 10 distinct chapters providing a concise introduction to Big Data Analysis and recent Techniques and Environments for Big Data Analysis. It gives insight into how the expensive fitness evaluation of evolutionary learning can play a vital role in big data analysis by adopting Parallel, Grid, and Cloud computing environments. | ||
650 | 0 | _aEngineering. | |
650 | 0 | _aData mining. | |
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aEngineering. |
650 | 2 | 4 | _aComputational Intelligence. |
650 | 2 | 4 | _aData Mining and Knowledge Discovery. |
650 | 2 | 4 | _aArtificial Intelligence (incl. Robotics). |
700 | 1 |
_aMishra, Bhabani Shankar Prasad. _eeditor. |
|
700 | 1 |
_aDehuri, Satchidananda. _eeditor. |
|
700 | 1 |
_aKim, Euiwhan. _eeditor. |
|
700 | 1 |
_aWang, Gi-Name. _eeditor. |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer eBooks | |
776 | 0 | 8 |
_iPrinted edition: _z9783319275185 |
830 | 0 |
_aStudies in Big Data, _x2197-6503 ; _v17 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-27520-8 |
912 | _aZDB-2-ENG | ||
942 | _cEBK | ||
999 |
_c57383 _d57383 |