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Network Traffic Enhancement Through Proactive Caching By Mining Mainstream Media

Network Traffic Enhancement Through Proactive Caching By Mining Mainstream Media. Angelique Taylor Paige Gentry. Over View. Goal recap Implementation Experimental Results Conclusion Questions & Answers. Goal Recap.

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Network Traffic Enhancement Through Proactive Caching By Mining Mainstream Media

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  1. Network Traffic Enhancement Through Proactive Caching By Mining Mainstream Media Angelique Taylor Paige Gentry

  2. Over View • Goal recap • Implementation • Experimental Results • Conclusion • Questions & Answers

  3. Goal Recap • Our goal is to implement framework, to predict network traffic by mining mainstream news articles • Method • Latent Dirichlet Allocation (LDA) identifies and classifies popular topics in articles • ISP can query and pre-cache highly popular videos to reduce overall traffic and delay

  4. Implementation • Implemented a python program to parse the news articles and collect the title and content • Original LDA implementation processed random Wikipedia articles, we modified it to pass and process news articles. • Wrote a script to extract and store YouYube statistical data such as, view-counts, number of subscribers, YouTube ID’s, date of upload, user profile data, etc.

  5. Implementation Cont. • Wrote and implemented a program to sort topics by popularity , we pick most popular topics and compare it with news websites • Popular news websites (such as CNN, BBC) generate popularity chart over time by click-view data • Implemented the ZOOM Operation • Wrote a program to distribute the articles by sources/category • Query words using frequent pattern mining and LDA results to check relevancy and accuracy of popular topics

  6. Workflow

  7. Experimental Results (X axis) # of feeds VS (Y axis) Video relevance to the topic

  8. Experimental Results (X axis) # of feeds VS (Y axis) Accuracy of selecting video with most traffic

  9. Observation & Conclusion • Online LDA alone accurately chooses the most popular topic around 57% of the times using 1k articles. With 100k articles it is around 91% accurate. The blue line is the accuracy using both Online LDA and frequent pattern mining. With 1k articles the accuracy is around 92%. Using 100k articles the accuracy close to 100%. • When using only Online LDA there is only around a 60% chance the selected video will be relevant to the actual topic when using 10k articles. When using 100k articles the probability rises to about 87%. When using frequent pattern mining and Online LDA there is around a 94% chance the video selected is relevant using 10k articles. With 100k the probability is close to 100%. • From these results we conclude that using Online LDA combined with frequent pattern mining we will be able to predict popular topics from mainstream media and identify relevant videos from video portals with high accuracy

  10. Questions & answers • Thank you • Q&A!!

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