Detecting and extracting modifications from information sources
is an integral part of data warehousing.  For unsophisticated
sources, it is often necessary to infer modifications
by periodically comparing snapshots of data from the source.
Although this snapshot differential problem is closely related
to traditional joins, there are significant differences,
which lead to simple new algorithms.
In particular, we present algorithms that perform
compression of records.
We also present a window algorithm that works very well
if the snapshots are not ``very different.''
The algorithms are studied via analysis and an implementation of
two of them;
the results illustrate the potential gains achievable with the new
algorithms.