Using data mining techniques to extract key factors in Mobile live streaming 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 clientserver model, that the Internet was not designed to support multimedia applications, or even that mobile network isnxe2x80x99t 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 usersxe2x80x99 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.

A streaming approach to reveal crowded events from cellular data Abstract   Anomaly detection has been a very popular research topics over the last few years and applies to many scenarios from different disciplines. This research focuses on crowded phenomena, and addresses the detection of popular events by looking for xe2x80x9canomalousxe2x80x9d patterns in cellular traffic data. In particular, the paper elaborates upon previous proposals and presents two streaming algorithms based on the wavelet decomposition of traffic data. The new algorithms consume traffic samples as soon as they are available, elaborate the data in real time and possibly raise alarms upon threshold crossing. The effectiveness of the approach is assessed by using the public dataset containing the real cellular data acquired over the network of the most popular Italian traffic operator. The experiments prove that the streaming algorithms generally achieve performance comparable to that of their offline counterparts, and that the small degradation that may occasionally be observed is however well counterbalanced by the obvious advantage of detecting anomalies in realxe2x80x93time with no need to wait for the elaboration of overly long traffic timeseries.