[OPEN-ILS-GENERAL] Merging with on-order records

Dan Scott dan at coffeecode.net
Tue Sep 21 09:14:10 EDT 2010


Hi James:

On Mon, 2010-09-20 at 12:30 -0400, James Wagner wrote:
> Our supplier provides us with on-order stub records for our system.
> This
> allows patrons to put holds on items which have not yet arrived. Then
> when the items arrive we download and import the full marc records. In
> our previous system, when we did this, the stub records were
> automaticaly merged into the new, full record. This does seem to be
> what
> happens in Evergreen. Currently, we choose "Auto-Import Non-Colliding
> Records". This leaves us with those full records for which stubs
> exist.
> We then force Evegreen to import thses records. Now we have two
> records
> in the system. So we bring them up, send them to a bucket, and merge
> them (see Sitka Evergreen Training Manual, Chp. 20). If we have a
> hundred items arrive on the courier, we have to do this for each and
> every item. Is there a faster way? Should we change our workflow
> completely? Should we not use stub records, but instead ask our
> supplier
> for complete records? Any suggestions? 

I don't have many suggestions for you in the short term; we currently
work one record at a time. We try to start with a full record where
possible so that receiving is just a matter of applying the real barcode
and changing the status - but if we didn't have a full record when we
placed the order, we bring up the on-order record, select "Mark for
overlay", and then use Import via Z39.50 to overlay the full record on
our on-order record (or go with original cataloguing if a full record is
not available). 

This avoids database bloat from all of the ephemeral records that you
get from the merge dupe records strategy, but you're still working with
one record at a time.

In the longer term, the MARC Batch Import / Export in Evergreen 2.0
includes the new options:

Auto Merge/Overlay When Exactly 1 Match is Found
Merge/Overlay Profile

... which sound like a much better match for bulk use cases.



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