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OverviewThis 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. Full Product DetailsAuthor: Xiang Cheng , Luoyang Fang , Liuqing Yang , Shuguang CuiPublisher: Springer Nature Switzerland AG Imprint: Springer Nature Switzerland AG Edition: Softcover reprint of the original 1st ed. 2018 Dimensions: Width: 15.50cm , Height: 0.80cm , Length: 23.50cm Weight: 0.454kg ISBN: 9783030071455ISBN 10: 3030071456 Pages: 125 Publication Date: 12 January 2019 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsReviewsThe 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) 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) Author InformationTab Content 6Author Website:Countries AvailableAll regions |
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