Guide to big data applications
Title By: Srinivasan, S [Edited by]
Material type:
BookSeries: Studies in Big Data ; 26.Publisher: New York, NY : Springer Science+Business Media, c2018.Description: xvii, 565 p. : ill. ; 25 cm.ISBN: 9783319538167Subject(s): Engineering | Big dataDDC classification: 005.7 GU ID Online resources: Location Map | Item type | Home library | Call number | Status | Notes | Date due | Barcode | Item holds |
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| REGULAR | University of Wollongong in Dubai Main Collection | 005.7 GU ID (Browse shelf) | Available | Mar2018 | T0057894 |
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| 005.7 CO MA Man vs big data : | 005.7 EM PI Empirical studies on the development of executable business processes | 005.7 GR IN Integrated business information systems : | 005.7 GU ID Guide to big data applications | 005.7 HA ND Handbook of big data technologies | 005.7 HA SI Signal processing and networking for big data applications / | 005.7 KA MA Managing and processing big data in cloud computing |
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.
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 book is intended to help spur further innovation in big data. The research is presented in a way that allows readers, regardless of their field of study, to learn from how applications have proven successful and how similar applications could be used in their own field. Contributions stem from researchers in fields such as physics, biology, energy, healthcare, and business. The contributors also discuss important topics such as fraud detection, privacy implications, legal perspectives, and ethical handling of big data.