Andy Jezierski <[EMAIL PROTECTED]> schrieb am 04.04.2006 23:13:08:

> There have been numerous threads on how to have end users drop 
> misclassified mail to spam/ham folders in Exchange, but I don't recall 
> seeing any mention of a way of doing this with Notes. 
> 
> Is anyone doing this with Notes that would care to share the secret? 

I've implemented a side-wide sa-learn - but not user-specific as
the Notes network is most of the time not really compatible with
the smtp network you see from outside.

We have the following scenario:


            MX01<--loadbalancing-smtp-relays-->MX02
                          |                  |
                        NSEXT2<------>NSEXT3
                            \             /
---------DMZ-crossing-here-----DMZ-crossing-here--------
                                \       /
                                NSSPOKE
                                /    \\\\\\\\
                          NSAPP1        numerous other Notes server
                                |               |       |       |       |
                        <---Notes-clients-----Notes-clients---->


NSAPP1 hosts 2 DBs: SPAM.nsf and HAM.nsf
Enduser have a manual agent (java) coded by me which copies SPAM/HAM
to the DBs on NSAPP1 (Endusers only have write-access, but not read for
privacy reasons of delivered HAM mail).
NSSPOKE (the central server) pulls the DBs from NSAPP1.
NSSPOKE pushes the DBs to NSEXT2/3
The DBs on NSEXT2/3 have pop3 single-enabled with a special user
assigned to them.
MX01/02 are RHES4 running SA with exim.
The MX01/02 pull the messages from NSEXT2/3 via a fetchmail --invisible
(full fetch) keeping all mail on the server for consistency.
sa-learn forgets all bayes, learns the mails from the SPAM.nsf, learns
the mails from HAM.nsf.
Done.

This system might be possible to expand to user-specific bayes, but
this is unwanted in our case.

regards
        Sascha

--
Sascha Runschke
Netzwerk Administration
IT-Services

ABIT AG
Robert-Bosch-Str. 1
40668 Meerbusch

Tel.:+49 (0) 2150.9153.226
Mobil:+49 (0) 173.5419665
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