Big Data Analytics: Systems, Algorithms, Applications

• Κύριοι συγγραφείς: Prabhu, C.S.R (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Chivukula, Aneesh Sreevallabh (http://id.loc.gov/vocabulary/relators/aut), Mogadala, Aditya (http://id.loc.gov/vocabulary/relators/aut), Ghosh, Rohit (http://id.loc.gov/vocabulary/relators/aut), Livingston, L.M. Jenila (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Singapore : Springer Singapore : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Θέματα: Big data.; Data mining.; Big Data.; Data Mining and Knowledge Discovery.

 This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning - including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike. 

 Big Data -- Intelligent Systems -- Analytics Models for Data Science -- Big Data Tools - Hadoop Eco System -- Predictive Modeling for Unstructured Data -- Machine Learning Algorithms for Big Data -- Social Semantic Web Mining and Big Data Analytics -- Internet of Things (IoT) and Big Data Analytics -- Big Data Analytics for Financial and Services Banking -- Big Data Analytics Techniques in Capital Market Use Cases.