Understanding the growth pattern of content popularity has become a subject of immense interest to
Internet service providers, content makers and on-line advertisers. This understanding is also important for
the sustainable development of content distribution systems. As an approach to comprehend the characteristics of this growth pattern, a significant amount of research has been done in analyzing the popularity
growth patterns of YouTube videos. Unfortunately, no work has been done that intensively investigates the
popularity patterns of YouTube videos based on video object category. In this thesis, an in-depth analysis
of the popularity pattern of YouTube videos is performed, considering the categories of videos.
Metadata and request patterns were collected by employing category-specific YouTube crawlers. The
request patterns were observed for a period of five months. Results confirm that the time varying popularity
of di fferent YouTube categories are conspicuously diff erent, in spite of having sets of categories with very
similar viewing patterns. In particular, News and Sports exhibit similar growth curves, as do Music and
Film.
While for some categories views at early ages can be used to predict future popularity, for some others
predicting future popularity is a challenging task and require more sophisticated techniques, e.g., time-series clustering. The outcomes of these analyses are instrumental towards designing a reliable workload generator, which can be further used to evaluate diff erent caching policies for YouTube and similar sites. In this
thesis, workload generators for four of the YouTube categories are developed. Performance of these workload generators suggest that a complete category-specific workload generator can be developed using time-series clustering. Patterns of users' interaction with YouTube videos are also analyzed from a dataset collected in a local network. This shows the possible ways of improving the performance of Peer-to-Peer video distribution
technique along with a new video recommendation method.
| Identifer | oai:union.ndltd.org:USASK/oai:ecommons.usask.ca:10388/ETD-2013-08-1163 |
| Date | 2013 August 1900 |
| Contributors | Makaroff, Dwight J. |
| Source Sets | University of Saskatchewan Library |
| Language | English |
| Detected Language | English |
| Type | text, thesis |
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