Semantic Web Drools Module, Request for Feedbak
by Xavier Breton
Hi,
I'm looking for feedback, I'll develop a Semantic Web Drools Module that
will be the subject of my Master Degree Tesis.
The idea is to use Eclipse Modelling Framework (EMF) for prototyping and
follow a Model Driven Architecture (MDA) where the source language is
Semantic of Business Vocabularies and Business Rules (SBVR) and the target
language is Drools DRL.
The mapping could be (PIM level):
- Semantic Web Rule Language (SWRL)
- Ontology Web Language (OWL)
- RuleML
- Rule Interchange Format (RIF)
- REWERSE Rule Markup Language (R2ML)
It could be added to the module at the source UML or Entity Relationship
like models to transform the models into SBVR.
Regards
Xavier Breton
10 years, 9 months
Guided Editor in BRMS / Guvnor Version 5 (Snapshot of 26 June)
by Paul Browne
Folks,
For various reasons I'm trying out the Guided Editor for Business Rules in
the Guvnor Version 5 (Snapshot of 26 June from Hudson, deployed on JBoss App
Server 4.2.2GA).
I've created the Package / Category and uploaded a simple fact model (as
works in BRMS version 4). I create a new business rule using the guided
editor and the screen shows successfully with both 'When' and 'Then'
parts.Assume the next question is due to me missing something, but wanted to
double check:
When I press the green '+' to the right of the screen I am shown the message
/ dialog layer saying '
*Add a condition to the rule... *or* Add an action to the rule.
*Problem is that there doesn't appear to be a way of adding a condition or
action. The only thing I'm seeing in the logs is
* (Contexts.java:flushAndDestroyContexts:335) could not discover
transaction status
*Am I missing something or should I come back to Guvnor later in the
development Cycle?
Thanks
Paul
12 years, 8 months
Drools on android
by Justin King
Hi All,
I'm wondering if anyone has tried to use drools in a google android
application, and if so what problems did you have? I'd also be interested to
know if its even possible!
Thanks!
--
Regards,
Justin King
PhD Candidate
Faculty of Information and Communication Technologies
Swinburne University of Technology
http://www.ict.swin.edu.au/ictstaff/justinking
--
Regards,
Justin King
PhD Candidate
Faculty of Information and Communication Technologies
Swinburne University of Technology
http://www.ict.swin.edu.au/ictstaff/justinking
12 years, 10 months
Left and Right Unlinking - Community Project Proposal
by Mark Proctor
In an effort to help encourage those thinking of learning more about
the internals of rule engines. I have made a document on implementating
left and right unlinking. I describe the initial paper in terms relevant
to Drools users, and then how that can be implemented in Drools and a
series of enhancements over the original paper. The task is actually
surprisingly simple and you only need to learn a very small part of the
Drools implementation to do it, as such it's a great getting started
task. For really large stateful systems of hundreds or even thousands of
rules and hundreds of thousands of facts it should save significant
amounts of memory.
http://blog.athico.com/2010/08/left-and-right-unlinking-community.html
Any takers?
Mark
Introduction
The following paper describes Left and Right unlinking enhancements for
Rete based networks:
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6246
<http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.6246>
A rete based rule engine consists of two parts of the network, the alpha
nodes and the beta nodes. When an object is first inserted into the
engine it is discriminated against by the object type Node, this is a
one input and one output node. From there it may be further
discriminated against by alpha nodes that constrain on literal values
before reaching the right input of a join node in the beta part of the
network. Join nodes have two inputs, left and right. The right input
receives propagations consisting of a single object from the alpha part
of the network. The left input receives propagations consisting of 1 or
more objects, from the parent beta node. We refer to these propagating
objects as LeftTuple and RightTuple, other engines also use the terms
tokens or partial matches. When a tuple propagation reaches a left or
right input it's stored in that inputs memory and it attempts to join
with all possible tuples on the opposite side. If there are no tuples on
the opposite side then no join can happen and the tuple just waits in
the node's memory until a propagation from the opposite side attempts to
join with it. If a given. It would be better if the engine could avoid
populating that node's memory until both sides have tuples. Left and
right unlinking are solutions to this problem.
The paper proposes that a node can either be left unlinked or right
unlinked, but not both, as then the rule would be completely
disconnected from the network. Unlinking an input means that it will not
receive any propagations and that the node's memory for that input is
not populated, saving memory space. When the opposite side, which is
still linked, receives a propagation the unlinked side is linked back in
and receives all the none propagated tuples. As both sides cannot be
unlinked, the paper describes a simple heuristic for choosing which side
to unlink. Which ever side becomes empty first, then unlink the other.
It says that on start up just arbitrarily chose to unlink one side as
default. The initial hit from choosing the wrong side will be
negligible, as the heuristic corrects this after the first set of
propagations.
If the left input becomes empty the right input is unlink, thus clearing
the right input's memory too. The moment the left input receives a
propagation it re-attaches the right input fully populating it's memory.
The node can then attempt joins as normal. Vice-versa if the right input
becomes empty it unlinks the left input. The moment the right input
receives a propagation it re-attaches the left input fully populating
it's memory so that the node can attempt to join as normal.
Implementing Left and Right Unlinking for shared Knowledge Bases
The description of unlinking in the paper won't work for Drools or for
other rule engines that share the knowledge base between multiple
sessions. In Drools the session data is decoupled from the main
knowledge base and multiple sessions can share the same knowledge base.
The paper above describes systems where the session data is tightly
coupled to the knowledge base and the knowledge base has only a single
session. In shared systems a node input that is empty for one session
might not be empty for another. Instead of physically unlinking the
nodes, as described in the paper, an integer value can be used on the
session's node memory that indicates if the node is unlinked for left,
right or both inputs. When the propagating node attempts to propagate
instead of just creating a left or right tuple and pushing it into the
node. It'll first retrieve the node's memory and only create the tuple
and propagate if it's linked.
This is great as it also avoids creating tuple objects that would just
be discarded afterwards as there would be nothing to join with, making
things lighter on the GC. However it means the engine looks up the node
memory twice, once before propagating to the node and also inside of the
node as it attempt to do joins. Instead the node memory should be looked
up once, prior to propagating and then passed as an argument, avoiding
the double lookup.
Traditional Rete has memory per alpha node, for each literal constraint,
in the network. Drools does not have alpha memory, instead facts are
pulled from the object type node. This means that facts may needlessly
evaluate in the alpha part of the network, only to be refused addition
to the node memory afterwards. Rete supports something called "node
sharing", where multiple rules with similar constructs use the same
nodes in the network. For this reason shared nodes cannot easily be
unlinked. As a compromise when the alpha node is no longer shared, the
network can do a node memory lookup, prior to doing the evaluation and
check if that section of the network is unlinked and avoid attempting
the evaluation if it is. This allows for left and right unlinking to be
used in a engine such as Drools.
Using Left and Right Unlinking at the Same Time
The original paper describes an implantation in which a node cannot have
both the left and right inputs unlinked for the same node. Building on
the extension above to allow unlinking to work with a shared knowledge
base the initial linking status value can be set to both left and right
being unlinked. However in this initial state, where both sides are
unlinked, the leaf node's right input isn't just waiting for a left
propagation so the right can re-link itself (which it can't as the left
is unlinked too). It's also waiting to receive it's first propagation,
when it does it will link the left input back in. This will then tell
it's parent node's right input to also do the same, i.e. wait for it's
first right input propagation and link in the left when it happens. If
it already has a right propagation it'll just link in the left anyway.
This will trickle up until the root is finally linked in and
propagations can happen as normally, and the rule's nodes return to the
above heuristics for when to link and unlink the nodes.
Avoid Unnecessary Eager Propagations
A rule always eagerly propagates all joins, regardless of whether the
child node can undertake joins too, for instance of there is no
propagates for the leaf node then no rules can fire, and the eager
propagations are wasted work. Unlinking can be extended to try to
prevent some level of eager propagations. Should the leaf node become
right unlinked and that right input also become empty it will unlink the
left too (so both sides are unlinked) and go back to waiting for the
first right propagation, at which point it'll re-link the left. If the
parent node also has it's right input unlinked at the point that it's
child node unlinks the left it will do this too. It will repeat this up
the chain until it reaches a node that has both left and right linked
in. This stops any further eager matching from occurring that we know
can't result in an activation until the leaf node has at least one right
input.
Heuristics to Avoid Churn from Excessive and Unnecessary Unlinking
The only case where left and right linking would be a bad idea is in
situations that would cause a "churn". Churn is when a node with have a
large amount of right input memory is continually caused to be linked in
and linked out, forcing those nodes to be repeatedly populated which
causes a slow down. However heuristics can be used here too, to avoid
unnecessary unlinking. The first time an input becomes empty unlink the
opposite and store a time stamp (integer counter for fact handles from
the WM). Then have a minimum delta number, say 100. The next time it
attempts to unlink, calculate the delta of the current time stamp
(integer counter on fact handle) and the time stamp of the node which
last unlinked (which was recorded at the point of unlinking) if it's
less than 100 then do nothing and don't unlink until it's 100 or more.
If it's 100 or more then unlink and as well as storing the unlink time
stamp, then take the delta of 100 or more and apply a multiple (2, 3, 4
etc depending on how steep you want it to rise, 3 is a good starting
number) and store it. Such as if the delta is 100 then store 300. The
next time the node links and attempts to unlink it must be a delta of
300 or more, the time after that 900 the time after that 2700.
14 years
Accessing Drools compiled classes
by Guillaume Sauthier
Hi team
I've tried the IRC (without much success I admit), maybe here someone
will have some thoughts to share :)
I'm looking for a way to "intercept" the classes being generated by the
drools compiler.
I've seen that the classes bytecode is stored deep in
PackageStore/JavaDialectRuntimeData, so deep that I cannot easily access
it :)
The objective is to be able to give theses classes to Bnd (I want to
store all of that in an OSGi bundle) so that appropriate Import-Packages
can be computed. That will avoid to have DynamicImport-Packages all
around my bundles :)
Currently, what I get from the drools compiler is a
Collection<KnowledgePackage> but I have no API (or didn't find any) to
access (or know) the classes generated by the compiler.
Any ideas ?
Thanks
--Guillaume
14 years
backward chaining
by Christine Karman
Hi,
we're using Drools in a commercial project. Yesterday a coworker
realized that drools doesn't support backward chaining. I have used
drools before in several projects, but apparently I never noticed that
either. We do need backward chaining in our project. Is this a feature
that is planned? Is there anything we can do to help create it?
dagdag
Christine
--
dagdag is just a two-character rotation of byebye.
14 years
exploring functional programming
by Mark Proctor
We are thinking of moving "accumulate" to a simple "for" keyword. We
might allow 'in' and 'from' to be used interchangably and allow ';' semi
colons to separate the sections. I'm also wondering ho we could allow
function pipelines for the function part of the element. We also need
type inference and "default" return values.
So here are some code snippets to show what I'm thinking, as
improvements over what we do with accumulate now.
// Simple 'or' very simlar to accumulate before. ; for section
separating. With a new boolean section at the end to decide whether to
propagate or not. Will probably use '=' for function assignments.
$c : Customer()
for( $bi : BasketItem( customer == $c );
$s = sum( $bi.price);
$s > 100 )
// Multiple functions are ofcourse allowed
$c : Customer()
for( $bi : BasketItem( customer == $c );
$mn =min( $bi.price),
$mx = max( $bi.price); )
// As are multiple patterns, and as before patterns can come 'from' an
expression and with type inference we can get some nice compact text:
for ( x in [0..4]
y in [0..4]
$c : Cell( row == y, col == x );
$avg = avg( $cell.value );
$avg > 100 )
The above is possible now with the following:
Integer( this > 100) from
accumulate( x : Integer() from [0, 1, 2, 3, 4]
y : Integer() from [0, 1, 2, 3, 4],
$c : Cell( row == y, col == x ),
avg( $c.value) )
I think the proposed additions reall help with declarative readability.
The normal chaining of elements is supported:
$c : Customer()
for( $b1 : BasketItem() in
for( $b2 : BasketItem( customer == $c );
filter( $b2, $b2.price > 100); );
$a = avg( $b1.price ); )
'for' will have a default return type for each bound function. In this
case it returns a single value $a, if there are multiple bound results
an array/map must be used for access.
I also think we should allow pipelineing of functions, much as you would
do in a normal functional programming, possibly using haskell like
"stream fusion" capable functions.
// '$' refers to the magic contents of the function which is "piped" in.
So $bi is piped to map, $ refers to each value evaluated in the
function, with type inference. 'map' results are piped to 'filter'. The
final results are assigned to $r.
$c : Customer()
for( $bi : BasketItem( customer == $c );
$r = $bi | map( $, $.price * 2) |
filter( $, $.price < 100);
$r.size > 100 )
More ideas welcome :) But I think this opens up some very interesting
areas for DRL, with something that will hopefully feel more natural for
developers.
Mark
14 years
