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.

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.

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.