[rules-users] Rules-users list delays/duplicates

[Greg Barton]

Yeah, I've gotten about 40 old messages today.  Also I replied to a message today and haven't seen it sent back to me yet.

Well, why am I even replying to this, then. :)  If nobody sees this message until November, I hope you all went to ORF2009. :D

--- On Fri, 5/1/09, Ingomar Otter <iotter at mac.com> wrote:

> From: Ingomar Otter <iotter at mac.com>
> Subject: [rules-users] Rules-users list delays/duplicates
> To: "Rules Users List" <rules-users at lists.jboss.org>
> Date: Friday, May 1, 2009, 12:02 PM
> It seems that the rules-users list is experiencing either
> massive delays or duplicate delivery.
> See message below which reached me today.
> Should you experience the same effect, I have indicated
> this to  "rules-users-owner" so it is known
> 
> Cheers,
>   Ingomar
> 
> > 
> > Von: David Boaz <davidb at dbmotion.com>
> > Datum: 17. August 2008 17:22:00 MESZ
> > An: rules-users at lists.jboss.org
> > Betreff: [rules-users] CEP functionality
> > Antwort an: Rules Users List
> <rules-users at lists.jboss.org>
> > 
> > 
> > Dear all,
> > 
> > We posted this question two weeks ago, but with no
> replies. please forgive
> > me for posting it again.
> > 
> > In our business, most of the facts have time-stamp
> attributes. For example,
> > each laboratory test (a fact) has the time stamp when
> the sample was taken,
> > and when the machine produced the result value. In our
> business-logic, we
> > often analyze the relation between facts using these
> attributes.
> > 
> > For example:
> > 1) finding two adjacent labResults with an increasing
> value
> > 2) checking whether 3 facts are consecutive
> > 3) counting the number of facts fulfilling a specific
> constraint within a
> > specific time window.
> > 
> > We were happy to read (in the blog) about the new CEP
> feature which is about
> > to be added to drools 5.0.
> > 
> > * Where can we find a more detailed description of CEP
> functionality in
> > drools?
> > * Is it a good idea to use CEP for analyzing historic
> data (retrieved from
> > the database)? or is it best for online data.
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