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We contribute a scalable implementation of Ryoo et al's Pooled Time Seriesalgorithm from CVPR 2015. The updated algorithm has been evaluated on a largeand diverse dataset of approximately 6800 videos collected from a crawl of thedeep web related to human trafficking on DARPA's MEMEX effort. We describe theproperties of Pooled Time Series and the motivation for using it to relatevideos collected from the deep web. We highlight issues that we found whilerunning Pooled Time Series on larger datasets and discuss solutions for thoseissues. Our solution centers are re-imagining Pooled Time Series as aHadoop-based algorithm in which we compute portions of the eventual solution inparallel on large commodity clusters. We demonstrate that our new Hadoop-basedalgorithm works well on the 6800 video dataset and shares all of the propertiesdescribed in the CVPR 2015 paper. We suggest avenues of future work in theproject.

Scalable Pooled Time Series of Big Video Data from the Deep Web