Yongzhe Wang
Authored Publications
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Streaming Trends: A Low-Latency Platform for Dynamic Video Grouping and Trending Corpora Building
Scott Wang
Caroline Zhou
Ashkan Norouzi Fard
Yaping Zhang
Li Zhang
Mingyan Gao
Qiao Zhang
2025
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This paper presents Streaming Trends, a real-time system deployed on a short-form videos platform that enables dynamic content grouping, tracking videos from upload to their identification as part of a trend. Addressing the latency inherent in traditional batch processing for short-form video, Streaming Trends utilizes online clustering and flexible similarity measures to associate new uploads with relevant groups in near real-time. The system combines online processing for immediate updates triggered by uploads and seed queries with offline processes for similarity modeling and cluster quality maintenance. By facilitating the rapid identification and association of trending videos, Streaming Trends significantly enhances content discovery and user engagement on the YouTube Shorts platform.
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A cloud-based large-scale distributed video analysis system
Wei-Ta Chen
Huahui Wu
Anil Kokaram
Jaron Schaeffer
IEEE International Conference on Image Processing (2016)
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Digital content consumption is exploding thanks to the advances of the distributed cloud-computing infrastructures and the consumer electronics. Further challenges have been posed to engineers and researchers to satisfy the ever increasing user needs not only for high quality video delivery, but also for richer experience. In order to support various video analysis tasks in addition to transcoding, a software platform is designed based on the Google cloud computing infrastructure with the features to be flexible, scalable, robust, and secure. In this paper, we discuss the scope, requirements, constraints, features of such a system, the problems we met and how they are resolved.
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