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Mobile Big Data Softcover Reprint of the Original 1st 2018 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 125 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 36 Illustrations, color; 1 Illustrations, black and white; XII, 125 p. 37 illus., 36 illus. in color., 1 Paperback / softback
  • Sari: Wireless Networks
  • Ilmumisaeg: 12-Jan-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030071456
  • ISBN-13: 9783030071455
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Mobile Big Data Softcover Reprint of the Original 1st 2018 ed.Suurem pilt
  • Formaat: Paperback / softback, 125 pages, kõrgus x laius: 235x155 mm, kaal: 454 g, 36 Illustrations, color; 1 Illustrations, black and white; XII, 125 p. 37 illus., 36 illus. in color., 1 Paperback / softback
  • Sari: Wireless Networks
  • Ilmumisaeg: 12-Jan-2019
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030071456
  • ISBN-13: 9783030071455
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783030071455.html

This book  provides a comprehensive picture of mobile big data starting from data sources to mobile data driven applications. Mobile Big Data comprises two main components: an overview of mobile big data, and the case studies based on real-world data recently collected by one of the largest mobile network carriers in China.

 In the first component, four areas of mobile big data life cycle are surveyed: data source and collection, transmission, computing platform and applications. In the second component, two case studies are provided, based on the signaling data collected in the cellular core network in terms of subscriber privacy evaluation and demand forecasting for network management. These cases respectively give a vivid demonstration of what  mobile big data looks like, and how it can be analyzed and mined to generate useful and meaningful information and knowledge.

 This book targets researchers, practitioners and professors relevant to this field.  Advanced-level students studying computer science and electrical engineering will also be interested in this book as supplemental reading. 


Arvustused

The study shows that the rich set of smartphone sensors currently available enables user identification across datasets collected from different mobile networks. This text is best suited to graduate-level computer science and engineering students, and to professionals. Summing Up: Recommended. Graduate students, faculty, and professionals. (C. Tappert, Choice, Vol. 46 (9), May, 2019)

1 Mobile Big Data.- 2 Source and Collection.- 3 Transmission.- 4 Computing.- 5 Applications.- 6 Case Study: Demand Forecasting for Predictive Network Managements.- 7 Case Study: User Identification for Mobile Privacy.