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Mathematical Problems in Engineering
Volume 2016, Article ID 2541814, 5 pages
http://dx.doi.org/10.1155/2016/2541814
Research Article

Mining the IPTV Channel Change Event Stream to Discover Insight and Detect Ads

Laboratory for Telecommunications, Faculty of Electrical Engineering, University of Ljubljana, Trzaska cesta 25, SI-1000 Ljubljana, Slovenia

Received 24 December 2015; Accepted 23 February 2016

Academic Editor: Zoran Obradovic

Copyright © 2016 Matej Kren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

IPTV has been widely deployed throughout the world, bringing significant advantages to users in terms of the channel offering, video on demand, and interactive applications. One aspect that has been often neglected is the ability of precise and unobtrusive telemetry. TV set-top boxes that are deployed in modern IPTV systems can be thought of as capable sensor nodes that collect vast amounts of data, representing both the user activity and the quality of service delivered by the system itself. In this paper we focus on the user-generated events and analyze how the data stream of channel change events received from the entire IPTV network can be mined to obtain insight about the content. We demonstrate that it is possible to predict the occurrence of TV ads with high probability and show that the approach could be extended to model the user behavior and classify the viewership in multiple dimensions.