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020 _a9783031556395
_9978-3-031-55639-5
024 7 _a10.1007/978-3-031-55639-5
_2doi
050 4 _aQA76.9.B45
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
100 1 _aDemirbaga, Ümit.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976480
245 1 0 _aBig Data Analytics
_h[electronic resource] :
_bTheory, Techniques, Platforms, and Applications /
_cby Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXXIII, 284 p. 81 illus., 78 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aIntroduction -- Big Data -- Big Data Analytics -- Cloud Computing for Big Data Analytics -- Big Data Analytics Platforms -- Big Data Storage Solutions -- Big Data Monitoring -- Debugging Big Data Systems for Big Data Analytics -- Machine Learning for Big Data Analytics -- Real-World Big Data Analytics Case Studies -- Big Data Analytics in Smart Grids -- Big Data Analytics in Bioinformatics.
520 _aThis book introduces readers to big data analytics. It covers the background to and the concepts of big data, big data analytics, and cloud computing, along with the process of setting up, configuring, and getting familiar with the big data analytics working environments in the first two chapters. The third chapter provides comprehensive information on big data processing systems - from installing these systems to implementing real-world data applications, along with the necessary codes. The next chapter dives into the details of big data storage technologies, including their types, essentiality, durability, and availability, and reveals their differences in their properties. The fifth and sixth chapters guide the reader through understanding, configuring, and performing the monitoring and debugging of big data systems and present the available commercial and open-source tools for this purpose. Chapter seven gives information about a trending machine learning, Bayesian network: a probabilistic graphical model, by presenting a real-world probabilistic application to understand causal, complex, and hidden relationships for diagnosis and forecasting in a scalable manner for big data. Special sections throughout the eighth chapter present different case studies and applications to help the readers to develop their big data analytics skills using various big data analytics frameworks. The book will be of interest to business executives and IT managers as well as university students and their course leaders, in fact all those who want to get involved in the big data world.
650 0 _aBig data.
_957216
650 0 _aQuantitative research.
650 0 _aElectric power distribution.
_966068
650 0 _aMedical care.
650 0 _aMachine learning.
_955059
650 1 4 _aBig Data.
_957216
650 2 4 _aData Analysis and Big Data.
650 2 4 _aEnergy Grids and Networks.
_966069
650 2 4 _aHealth Care.
_966563
650 2 4 _aMachine Learning.
_955059
700 1 _aAujla, Gagangeet Singh.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aJindal, Anish.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976481
700 1 _aKalyon, Oğuzhan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_976482
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031556388
776 0 8 _iPrinted edition:
_z9783031556401
776 0 8 _iPrinted edition:
_z9783031566981
856 4 0 _uhttps://doi.org/10.1007/978-3-031-55639-5
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
999 _c59695
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