This is working off of the new CSV format from the new AMT interface, which has one row per assignment:

a = read.csv("amt_assignments_file.csv")
# some lame datetime cleanups - amazon uses strftime("%c"), totally dumb...
lame_convert < - function(x)  strptime(x, "%A %b %d %T")
for (c in c('AcceptTime','AutoApprovalTime','CreationTime','Expiration','SubmitTime'))
 a[,c] = as.POSIXct( lame_convert(a[,c]) )

Here’s the parallelism plot:

worker_parallelism_plot < - function(a, w_pos=NULL, ...) {
  if (is.null(w_pos)) {
    w_starts = (dfagg(a,a$WorkerId, function(x) min(x$SubmitTime - x$WorkTimeInSeconds)))
    w_pos = rank(w_starts, ties='first')
  }

  plot(a$SubmitTime - a$WorkTimeInSeconds, w_pos[a$WorkerId],  type='p', ...)
  segments(a$SubmitTime - a$WorkTimeInSeconds, w_pos[a$WorkerId],   a$SubmitTime, w_pos[a$WorkerId])
  # text(sort(w_starts), 1:length(w_pos), sprintf("%s", 1:length(w_pos)), pos=2)
}

The one-box-per-worker plot in the other post is just

library(lattice)
xyplot(WorkTimeInSeconds ~ SubmitTime | WorkerId, data=a)

Have you thought of examining more closely the HITs that tend to take longer to complete than the rest? I am wondering if they are “more difficult” than the rest, or if they fall into some specific category.