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180802 ||| eng |
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|a 9783319923123
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| 100 |
1 |
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|a Ouyang, Ye
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| 245 |
0 |
0 |
|a Mining Over Air: Wireless Communication Networks Analytics
|h Elektronische Ressource
|c by Ye Ouyang, Mantian Hu, Alexis Huet, Zhongyuan Li
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| 250 |
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|a 1st ed. 2018
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2018, 2018
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| 300 |
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|a XI, 196 p. 72 illus., 51 illus. in color
|b online resource
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| 505 |
0 |
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|a Wireless Networks -- Artificial Intelligence -- Big Data -- Machine Learning -- Long Term Evolution (LTE) -- The 5th Generation (5G) -- Self-Organizing Networks (SON) -- Quality of Experience (QoE) -- Network Performance -- Data Analytics
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| 653 |
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|a Computer Communication Networks
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| 653 |
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|a Algorithms
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| 653 |
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|a Data mining
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| 653 |
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|a Computer networks
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 700 |
1 |
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|a Hu, Mantian
|e [author]
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| 700 |
1 |
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|a Huet, Alexis
|e [author]
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| 700 |
1 |
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|a Li, Zhongyuan
|e [author]
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| 041 |
0 |
7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
5 |
0 |
|a 10.1007/978-3-319-92312-3
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| 856 |
4 |
0 |
|u https://doi.org/10.1007/978-3-319-92312-3?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
0 |
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|a 004.6
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| 520 |
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|a This book introduces the concepts, applications and development of data science in the telecommunications industry by focusing on advanced machine learning and data mining methodologies in the wireless networks domain. Mining Over Air describes the problems and their solutions for wireless network performance and quality, device quality readiness and returns analytics, wireless resource usage profiling, network traffic anomaly detection, intelligence-based self-organizing networks, telecom marketing, social influence, and other important applications in the telecom industry. Written by authors who study big data analytics in wireless networks and telecommunication markets from both industrial and academic perspectives, the book targets the pain points in telecommunication networks and markets through big data. Designed for both practitioners and researchers, the book explores the intersection between the development of new engineering technology and uses data from the industry to understand consumer behavior. It combines engineering savvy with insights about human behavior. Engineers will understand how the data generated from the technology can be used to understand the consumer behavior and social scientists will get a better understanding of the data generation process
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