CLUENET: Enabling automatic video aggregation in social media networks

Abstract

Similar contents, duplicates, and segments of videos abound in social media networks. However, querying and aggregating all these data with high quality and personalized demand present increasingly formidable challenges. In the paper, we propose a novel framework for automatically aggregating semantically similar and contextual videos in social media network, which called CLUENET. We use a proactive method to collect and integrate all-around valuable clues centered a video to improve the quality of aggregation; By use of these clues the CLUENET constructs a clues network for video aggregation which extract sequences and similar contents of videos, and uses dynamic Petri net (DPN) to steer video aggregation and data prefetching for adapted to different user's personalized demand. The main features of this framework and how it was implemented using state-of-the-art technologies are also introduced. © 2011 Springer-Verlag Berlin Heidelberg.