04323nam a22006015i 4500 978-3-031-55639-5 DE-He213 20251030112031.0 cr nn 008mamaa 240507s2024 sz | s |||| 0|eng d 9783031556395 978-3-031-55639-5 10.1007/978-3-031-55639-5 doi QA76.9.B45 UN bicssc COM021000 bisacsh UN thema Demirbaga, Ümit. author. aut http://id.loc.gov/vocabulary/relators/aut 76480 Big Data Analytics [electronic resource] : Theory, Techniques, Platforms, and Applications / by Ümit Demirbaga, Gagangeet Singh Aujla, Anish Jindal, Oğuzhan Kalyon. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XXIII, 284 p. 81 illus., 78 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Introduction -- 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. This 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. Big data. 57216 Quantitative research. Electric power distribution. 66068 Medical care. Machine learning. 55059 Big Data. 57216 Data Analysis and Big Data. Energy Grids and Networks. 66069 Health Care. 66563 Machine Learning. 55059 Aujla, Gagangeet Singh. author. aut http://id.loc.gov/vocabulary/relators/aut Jindal, Anish. author. aut http://id.loc.gov/vocabulary/relators/aut 76481 Kalyon, Oğuzhan. author. aut http://id.loc.gov/vocabulary/relators/aut 76482 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031556388 Printed edition: 9783031556401 Printed edition: 9783031566981 https://doi.org/10.1007/978-3-031-55639-5 ZDB-2-SMA ZDB-2-SXMS 59695 59695