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