By "performance of RETE" what are you referring to?

There are many aspects of RETE, which one must study carefully. It's
good that you're translating RDF to OWL, but the larger question is
why use OWL/RDF in the first place? Unless the knowledge easily fits
into axioms like "sky is blue" or typical RDF examples, there's no
benefit to storing or using RDF. My own bias perspective on RDF/OWL.

The real question isn't "should I use RETE or how does RETE perform".
The real question is "how do I solve the problem efficiently?"

I've built compliance engines for trading systems using JESS. I can
say from first hand experience, it's how you use the engine that has
the biggest factor. I've done things like load 500K records to check
compliance across a portfolio set with minimal latency for nightly
batch processes. the key though is taking time to study existing
literature and understanding things before jumping to a solution.

providing concrete examples of what your doing will likely get better
advice than making general statements.


On Thu, Jun 9, 2011 at 12:17 PM, Md Oliya <md.ol...@gmail.com> wrote:
> Thank you very much Peter for the useful information. I will definitely look
> into that.
> but in the context of this message, i am not loading a huge (subjective
> interpretation?) knowledge base. It's 100k assertions, with the operations
> taking around 400 MB.
> Secondly, in my experiments, I subtracted the loading time of the
> assertions/retractions in jess, as I'm focusing on the performance of the
> Rete.
> Lastly, I am not doing an RDF based mapping; rather, I follow the method of
> Description Logic Programs for translating each Class/Property of OWL into
> its corresponding template.
>
>
> --Oli.
>
>
> On Fri, Jun 10, 2011 at 12:03 AM, Peter Lin <wool...@gmail.com> wrote:
>>
>> Although it "may" be obvious to some people, I thought I'd mention
>> this well known lesson.
>>
>> Do not load huge knowledge base into memory. This lesson is well
>> documented in existing literature on knowledge base systems. it's also
>> been discussed on JESS mailing list numerous times over the years, so
>> I would suggest searching JESS mailing list to learn from other
>> people's experience.
>>
>> It's better to intelligently load knowledge base into memory as
>> needed, rather than blindly load everything. Even in the case where
>> someone has 256Gb of memory, one should ask "why load all that into
>> memory up front".
>>
>> If the test is using RDF triples, it's well known that RDF triples
>> produces excessive partial matches and often results in
>> OutOfMemoryException. The real issue isn't JESS, it's how one tries to
>> solve a problem. I would recommend reading Gary Riley's book on expert
>> systems to avoid repeating a lot of mistakes that others have already
>> documented.
>>
>>
>> On Thu, Jun 9, 2011 at 11:41 AM, Md Oliya <md.ol...@gmail.com> wrote:
>> > Thank you Ernest.
>> > I am experimenting with the Lehigh university benchmark, where i
>> > transfer
>> > OWL TBox into their equivalent rules in Jess, with the logical
>> > construct.
>> > Specifically, I am using the dataset and transformations, as used in the
>> > OpenRuleBench.
>> > As for the runtimes, I missed a point about the retractions. The fact
>> > is,
>> > even if the session does not contain any rules (no defrules, just
>> > assertions), loading the same set of retractions takes a considerable
>> > time.
>> > This indicates that the high runtime is mostly incurred by jess internal
>> > operations.
>> > but still, when the number of changes grows high (say more than 10%) the
>> > runtime is not acceptable, and rerunning with the retracted kb would be
>> > faster.
>> > I have another question as well: what type of truth maintenance method
>> > is
>> > implemented in jess? Do you solely rely on the Rete memory nodes and
>> > tokens
>> > for this purpose?
>> >
>> > --Oli.
>> >
>> >
>> > On Mon, Jun 6, 2011 at 7:37 PM, Ernest Friedman-Hill
>> > <ejfr...@sandia.gov>
>> > wrote:
>> >>
>> >> I don't think there's a particular reason in general. Retracting a fact
>> >> takes only a little longer than asserting one, on average. But if we
>> >> assume
>> >> liberal use of "logical", retracting a single fact could result in a
>> >> sort of
>> >> "cascade effect" whereby retracting a single fact would result in many
>> >> other
>> >> facts, and many activations, being removed also due to dependencies.
>> >>  All of
>> >> that would take time.  Still, your case seems extreme. Maybe there's
>> >> something pathological about this particular case.
>> >>
>> >>
>> >> On Jun 5, 2011, at 3:18 PM, Md Oliya wrote:
>> >>
>> >>> Hi,
>> >>>
>> >>> I am doing some experiments with a set of rules which contain the
>> >>> "logical" CE.
>> >>> I intend to see the performance of Jess on a set of assertions as well
>> >>> as
>> >>> retractions.
>> >>>
>> >>> After some experiments, I found that the runtime for assertions is
>> >>> much
>> >>> less than that of retractions.
>> >>> In fact, the performance on retractions is so bad that I would rather
>> >>> re
>> >>> (run) jess on a retracted kb.
>> >>>
>> >>>
>> >>> A sample test case:
>> >>> The KB size,  number of assertions, number of retractions, and number
>> >>> of
>> >>> rules are 100K, 50K, 1k, and 100, respectively.
>> >>> runtimes are >> initial run: 860ms,  assertions:320ms --  retractions:
>> >>> 4s.
>> >>>
>> >>>
>> >>> Would you please give some hints on the reason?
>> >>>
>> >>>
>> >>> Thanks in advance.
>> >>> --Oli.
>> >>
>> >> ---------------------------------------------------------
>> >> Ernest Friedman-Hill
>> >> Informatics & Decision Sciences, Sandia National Laboratories
>> >> PO Box 969, MS 9012, Livermore, CA 94550
>> >> http://www.jessrules.com
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
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>>
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