Guide to Big Data Applications

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Srinivasan, S. (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Studies in Big Data, 26
• Θέματα: Electrical engineering.; Computer organization.; Signal processing.; Image processing.; Speech processing systems.; Big data.; Health informatics.; Mass media.; Law.; Communications Engineering, Networks.; Computer Systems Organization and Communication Networks.; Signal, Image and Speech Processing.; Big Data/Analytics.; Health Informatics.; IT Law, Media Law, Intellectual Property.

This handbook brings together a variety of approaches to the uses of big data in multiple fields, primarily science, medicine, and business. This single resource features contributions from researchers around the world from a variety of fields, where they share their findings and experience. This bo...

Πίνακας περιεχομένων:

• Introduction
• Big Data Analytics
• Big Data and Social Media
• Use of Cloud Computing for Big Data in Business
• Economic Data Analysis Related to Developing Countries
• High Performance Computing and Big Data
• Big Data Applications in Physics
• Big Data Applications in Chemistry
• Big Data Applications in Mathematics
• Big Data Applications in Biology
• Big Data Applications in Engineering
• Big Data Applications in Meteorology
• Big Data Applications in Environmental Science
• Big Data Applications in Energy
• Security Applications for Big Data
• Big Data Applications in Network Traffic Analysis
• Big Data Applications in Supply Chain Logistics
• Big Data Applications in Healthcare
• Big Data Applications in Cancer Research
• Impact of Big Data in Marketing
• Use of Big Data in Banking
• Using Big Data for Fraud Detection in Accounting
• Using Big Data for Supply Chain Management
• Privacy Implications of Big Data
• Legal Perspectives of Big Data
• Ethical Handling of Big Data in Practical Uses
• Conclusion.