Mobile Big Data
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Beschreibung
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.
Provides a comprehensive overview of mobile big data life cycle, including data source & collection, transmission, computing platform, and data-driven applications Includes two case studies in detail with the real-world collected mobile big data to vividly demonstrate the mobile big data analysis and mining in terms of subscriber privacy and demand forecasting Surveys the supporting infrastructure on communications and networks for mobile big data transmission
Autor*in
Xiang Cheng
Themen in »Mobile Big Data«
Mobile data Big data Spatiotemporal Multi-sensory Real-time Personalized data mining location privacy data source data collection transmission data-driven applications machine learning deep learning mobility
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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)
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