Using data mining techniques to extract key factors in Mobile live streaming

被引：2
|
作者：
Correa da Silva, Daniel Vasconcelos [1 ]
Velloso, Pedro Braconnot [2 ]
de Aragao Rocha, Antonio Augusto [3 ]
机构：
[1] Univ Fed Fluminense, Inst Fed Fluminense, Quissama, Brazil
[2] Univ Fed Rio de Janeiro, Rio De Janeiro, Brazil
[3] Univ Fed Fluminense, Niteroi, RJ, Brazil
来源：
2019 IEEE SYMPOSIUM ON COMPUTERS AND COMMUNICATIONS (ISCC) | 2019年
关键词：
live streaming; mobile network; mobile device;
D O I：
10.1109/iscc47284.2019.8969600
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In recent years many changes have taken place, such as increasingly powerful smartphones and cellular network allowing broadband access, and is expected in the coming years an increase in live streaming data traffic for mobile devices. On the other hand, it is common to find in the literature criticism of the traditional client-server model, that the Internet was not designed to support multimedia applications, or even that mobile network isn't appropriated to streaming, despite the fact that video streaming works and is a very popular Internet application. This work proposes, from the log files of a large CDN, discuss the influence of impact factors in the quality of the users' transmissions using mobile devices in popular live video transmissions. Using association rule, a data mining technique, this paper aims to analyze popular live streaming sessions to understand what factors may impact the broadcasts.
引用
收藏
页码：213 / 218
页数：6
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