Mobile Big Data

• Κύριοι συγγραφείς: Cheng, Xiang (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut), Fang, Luoyang (http://id.loc.gov/vocabulary/relators/aut), Yang, Liuqing (http://id.loc.gov/vocabulary/relators/aut), Cui, Shuguang (http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2018.
• Έκδοση: 1st ed. 2018.
• Σειρά: Wireless Networks,
• Θέματα: Computer communication systems.; Data mining.; Electrical engineering.; Artificial intelligence.; Computer Communication Networks.; Data Mining and Knowledge Discovery.; Communications Engineering, Networks.; Artificial Intelligence.

 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. . 

 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.