  Recent research shows that copying is prevalent for Deep-Web data and
considering copying can significantly improve truth finding from conflicting
values. However, existing copy detection techniques do not scale for large
sizes and numbers of data sources, so truth finding can be slowed down by one
to two orders of magnitude compared with the corresponding techniques that do
not consider copying. In this paper, we study {\em how to improve scalability
of copy detection on structured data}.
  Our algorithm builds an inverted index for each \emph{shared} value and
processes the index entries in decreasing order of how much the shared value
can contribute to the conclusion of copying. We show how we use the index to
prune the data items we consider for each pair of sources, and to incrementally
refine our results in iterative copy detection. We also apply a sampling
strategy with which we are able to further reduce copy-detection time while
still obtaining very similar results as on the whole data set. Experiments on
various real data sets show that our algorithm can reduce the time for copy
detection by two to three orders of magnitude; in other words, truth finding
can benefit from copy detection with very little overhead.
