This document provides an overview of rule engines and Drools rule engine concepts. It discusses why rule engines are used, how rules are defined in a rule engine using conditions and actions, and how a rule engine works with a working memory. It also covers Drools rule language concepts like patterns, bindings, and rule attributes. Additionally, it summarizes complex event processing, event stream processing capabilities in Drools Fusion, and temporal reasoning. Finally, it provides some Java programming notes and references for further reading.

1. Drools
Allan Huang @ Delta DRC

2. Rule Engine

3. Rule

4. Why Rules Engine?
 Rules are independent and each rule doesn’t know if
another rule even exists.
 A complex rule can be divided into simpler rules that
infer parts of information of a complex scenario and
feed it to the working memory, and other rules might
depend on that data to get activated.

5. Drools Rule Engine
 A rule engine is a piece of software, which having
some knowledge is able to perform conclusions.
 Knowledge and inferences are stored in rules, which
are called production rules.
 Production rules consist of conditions and actions,
which are executed when their conditions are true.
 A working memory, which stores all objects we may
want to use while making a decision.
 Then conflict resolution is provided by the agenda. It
arranges the order of actions, which has been
selected to be run.

6. Rule Engine

7. Hybrid Reasoning System
 Forward Chaining Engine – Data-driven
 Facts are asserted into working memory, which results in
one or more rules being concurrently true and scheduled
for execution by the Agenda.
 Backward Chaining Engine – Goal-driven
 We start with a conclusion which the engine tries to satisfy.
If it can't, then it searches for conclusions that it can
satisfy.
 It continues this process until either the initial conclusion
is proven or there are no more sub goals.
 Prolog is an example of a Backward Chaining engine.

8. Forward Chaining

9. Backward Chaining

10. Drools Rule Language
Drools Rule Language SQL Clause

11. Drools Concept Analogy
Drools Terms SQL Terms
 Session
 Entry Point (Fusion -
CEP)
 When Event (time == 1)
 Then
 Default Database
 Database
 Select, From, Where
 Trigger

12. DRL Terms
 Comment
 //dialect "mvel“
 /*dialect "mvel"*/
 Package
 package com.delta.rca.rules;
 Import
 import com.delta.rca.context.StaffPosition;
 Global
 global org.slf4j.Logger logger;
 Dialect
 dialect "java" or dialect "mvel"

13. DRL Attributes
 no-loop
 Setting no-loop to true will skip the creation of another Activation
for the rule with the current set of facts.
 salience
 Salience is a form of priority where rules with higher salience
values are given higher priority when ordered in the Activation
queue.
 Drools also supports dynamic salience.
 date-effective
 A rule can only activate after the date and time of the date-
effective attribute.
 date-expires
 A rule cannot activate if the current date and time is after the date-
expires attribute.
 duration
 The duration dictates that the rule will fire after a specified duration
if it is still true.

14. When (LHS) Syntax
 Pattern
 It can potentially match on each fact that is inserted in the
working memory.
 Pattern1( ) Pattern2( ) = Pattern1( ) and Pattern2( )
 Object( )
 matches all objects in the working memory
 Pattern Binding
 Person( $firstAge : age )
 The prefixed dollar symbol ($) is a convention.
 Property Access
 Person( age > 30 && < 40 )
 Person( name== "mark", address.( city == "london", country ==
"uk") )
 Person( $streetName : address!.street )
 Cheese( type matches "(Buffalo)?S*Mozarella" )

15. Advanced Conditional Elements
 Timers and Calendars
 Rules now support both interval and cron based timers, which replace the now
deprecated duration attribute.
 forall
 Evaluates to true when all facts that match the first pattern match all the remaining
patterns.
 from
 Enables users to specify an arbitrary source for data to be matched by LHS
patterns.
 collect
 Allows rules to reason over a collection of objects obtained from the given source
or from the working memory.
 accumulate
 accumulate is a more flexible and powerful form of collect.
 built-in accumulate functions
 average, min, max, count, sum, collectList, collectSet
 eval
 allows any semantic code (that returns a primitive boolean) to be executed.

16. Then (RHS) Syntax
 update(fact, facthandle)
 Informs the engine that an event or fact has changed and rules need to be
reconsidered.
 update(object)
 Is similar to above update method, the engine will look up the facthandle for you,
via an identity check.
 modify(facthandle) {propertyX=1, propertyY=2…}
 Is similar to above update method, not only update object itself, also its properties
be updated.
 insert(new Something())
 Inserts a new object into the Working Memory.
 insertLogical(new Something())
 Is similar to above insert method, but the object will be automatically retracted
when there are no more facts to support the truth of the currently firing rule.
 delete(facthandle)
 Removes an object from the Working Memory.
 retract(facthandle)
 Is similar to above delete method, but it deprecated!

17. Complex Event Processing
 Event Processing
 It is a method of tracking and analyzing (processing)
streams of information (data) about things that happen
(events),[1] and deriving a conclusion from them.
 Complex Event Processing (CEP)
 Combines data from multiple sources to infer events or
patterns that suggest more complicated circumstances.
 Its goal is to identifying the Meaningful Events and
respond to them as quickly as possible.

18. Event Stream Processing
 Event Stream Processing (ESP)
 Deals with the task of processing streams of event data
with the goal of identifying the meaningful pattern within
those streams
 Employs techniques such as detection of relationships
between multiple events, event correlation, event
hierarchies, and other aspects such as causality,
membership and timing.
 Semantic Events = Meaningful Events.

19. Drools Fusion
 It is the module responsible for adding Event Processing
Capabilities into the Drools platform for CEP / ESP.
 Support Events, with their proper semantics.
 Allow detection, correlation, aggregation and composition of
events.
 Support Temporal Constraints in order to model the temporal
relationships between events.
 Support Sliding Windows of interesting events.
 Sliding Time Windows
 Sliding Length Windows
 Support a session scoped unified clock.
 Real Time clock
 Pseudo clock
 …

20. Temporal Reasoning
 $eventA : EventA( this after[ 3m30s, 4m ] $eventB )
 3m30s <= $eventA.start - $eventB.end <= 4m
 $eventA : EventA( this during[ 5s, 10s ] $eventB )
 5s <= $eventA.start - $eventB.start <= 10s && 5s <= $eventB.end
- $eventA.end <= 10s
 $eventA : EventA( this meets[ 5s ] $eventB )
 abs( $eventB.start - $eventA.end) <= 5s
 $eventA : EventA( this overlaps[ 5s, 10s ] $eventB )
 $eventA.start < $eventB.start < $eventA.end < $eventB.end && 5s
<= $eventA.end - $eventB.start <= 10s
 $eventA : EventA( this starts[ 5s ] $eventB )
 abs( $eventA.start - $eventB.start ) <= 5s && $eventA.end <
$eventB.end
 Other Temporal Operators
 Before, Coincides, Finishes, Finished By, Includes, Met By,
Overlapped, Started By.

21. Java Notes
 Model Classes
 Add serialVersionUID
 Override the equals, hashCode, toString methods
 Use EqualsBuilder, HashCodeBuilder, ToStringBuilder classes
in Apache Commons Lang to override above methods
 Service
 Singleton or Static method
 AOP, e.g. Spring-based application

22. Reference
 Why Rules?
 Drools ~ Basic concepts
 Introduction to Drools: Rules fall from your eyes
 (PROCESSES%20&%20RULES)%20OR%20(RULES%
20&%20PROCESSES)%203/X
 Complex event processing
 Event stream processing
 Drools Documentation
 Chapter 5. Hybrid Reasoning
 Chapter 6: User Guide
 Chapter 7: Rule Language Reference
 Chapter 8. Complex Event Processing
 Chapter 20: Examples

23. Q&A