03941nam a22005175i 4500001001800000003000900018005001700027007001500044008004100059020001800100024003500118050001500153072001500168072002300183072001400206100008000220245017600300250001800476264006900494300006700563336002600630337002600656338003600682347002400718505039800742520159801140650001402738650002702752650003302779650001802812650002202830650001402852650003202866650003102898650001702929650002202946700008502968700007603053700008003129710003403209773002603243776003603269776003603305776003603341856004603377978-3-031-55639-5DE-He21320251030112031.0cr nn 008mamaa240507s2024 sz | s |||| 0|eng d a97830315563957 a10.1007/978-3-031-55639-52doi 4aQA76.9.B45 7aUN2bicssc 7aCOM0210002bisacsh 7aUN2thema1 aDemirbaga, Ümit.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut10aBig Data Analyticsh[electronic resource] :bTheory, Techniques, Platforms, and Applications /cby Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon. a1st ed. 2024. 1aCham :bSpringer Nature Switzerland :bImprint: Springer,c2024. aXXIII, 284 p. 81 illus., 78 illus. in color.bonline resource. atextbtxt2rdacontent acomputerbc2rdamedia aonline resourcebcr2rdacarrier atext filebPDF2rda0 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. 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. 0aBig data. 0aQuantitative research. 0aElectric power distribution. 0aMedical care. 0aMachine learning.14aBig Data.24aData Analysis and Big Data.24aEnergy Grids and Networks.24aHealth Care.24aMachine Learning.1 aAujla, Gagangeet Singh.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aJindal, Anish.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut1 aKalyon, Oğuzhan.eauthor.4aut4http://id.loc.gov/vocabulary/relators/aut2 aSpringerLink (Online service)0 tSpringer Nature eBook08iPrinted edition:z978303155638808iPrinted edition:z978303155640108iPrinted edition:z978303156698140uhttps://doi.org/10.1007/978-3-031-55639-5