Development Guide

Red Hat JBoss BRMS comprises the following components:

The Red Hat JBoss BRMS provides the following key features:

The Red Hat JBoss BPM Suite comprises the following components:

Red Hat JBoss BPM Suite provides the following features:

Red Hat JBoss BRMS comprises a high performance rule engine, a rule repository, easy to use rule authoring tools, and complex event processing rule engine extensions. The following use case describes how these features of Red Hat JBoss BRMS are implemented in insurance industry.

The consumer insurance market is extremely competitive, and it is imperative that customers receive efficient, competitive, and comprehensive services when visiting an online insurance quotation solution. An insurance provider increased revenue from their online quotation solution by upselling relevant, additional products during the quotation process to the visitors of the solution.

The diagram below shows integration of Red Hat JBoss BRMS with the insurance provider’s infrastructure. This integration is fruitful in such a way that when a request for insurance is processed, Red Hat JBoss BRMS is consulted and appropriate additional products are presented with the insurance quotation.

Figure 2.1. JBoss BRMS Use Case: Insurance Industry Decision Making

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Red Hat JBoss BRMS provides the decision management functionality, that automatically determines the products to present to the applicant based on the rules defined by the business analysts. The rules are implemented as decision tables, so they can be easily understood and modified without requiring additional support from IT.

This section describes a use case of deploying Red Hat JBoss BPM Suite to automate business processes (such as loan approval process) at a retail bank. This use case is a typical process-based specific deployment that might be the first step in a wider adoption of Red Hat JBoss BPM Suite throughout an enterprise. It leverages features of both business rules and processes of Red Hat JBoss BPM Suite.

A retail bank offers several types of loan products each with varying terms and eligibility requirements. Customers requiring a loan must file a loan application with the bank. The bank then processes the application in several steps, such as verifying eligibility, determining terms, checking for fraudulent activity, and determining the most appropriate loan product. Once approved, the bank creates and funds a loan account for the applicant, who can then access funds. The bank must be sure to comply with all relevant banking regulations at each step of the process, and has to manage its loan portfolio to maximize profitability. Policies are in place to aid in decision making at each step, and those policies are actively managed to optimize outcomes for the bank.

Business analysts at the bank model the loan application processes using the BPMN2 authoring tools (Process Designer) in Red Hat JBoss BPM Suite. Here is the process flow:

Business rules are developed with the rule authoring tools in Red Hat JBoss BPM Suite to enforce policies and make decisions. Rules are linked with the process models to enforce the correct policies at each process step.

The bank’s IT organization deploys the Red Hat JBoss BPM Suite so that the entire loan application process can be automated.

Figure 2.2. Loan Application Process Automation

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The entire loan process and rules can be modified at any time by the bank’s business analysts. The bank is able to maintain constant compliance with changing regulations, and is able to quickly introduce new loan products and improve loan policies in order to compete effectively and drive profitability.

The Rete implementation used in BRMS is called ReteOO. It is an enhanced and optimized implementation of the Rete algorithm specifically for object-oriented systems. The Rete Algorithm has now been deprecated, and PHREAK is an enhancement of Rete. However, Rete can still be used by developers. This section describes how the Rete Algorithm functions.

Rete Root Node

When using ReteOO, the root node is where all objects enter the network. From there, it immediately goes to the ObjectTypeNode.

Figure 5.1. ReteNode

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ObjectTypeNode

The ObjectTypeNode helps to reduce the workload of the rules engine. If there are several objects, the rule engine wastes a lot of cycles trying to evaluate every node against every object. To make things efficient, the ObjectTypeNode is used so that the engine only passes objects to the nodes that match the object’s type. This way, if an application asserts a new Account, it does not propagate to the nodes for the Order object.

In Red Hat JBoss BRMS, an inserted object retrieves a list of valid ObjectTypesNodes through a lookup in a HashMap from the object’s class. If this list does not exist, it scans all the ObjectTypeNodes to find valid matches. It then caches these matched nodes in the list. This enables Red Hat JBoss BRMS to match against any class type that matches with an instanceof check.

AlphaNodes

AlphaNodes are used to evaluate literal conditions. When a rule has multiple literal conditions for a single object type, they are linked together. This means that if an application asserts an Account object, it must first satisfy the first literal condition before it can proceed to the next AlphaNode.

AlphaNodes are propagated using ObjectTypeNodes.

Hashing

Red Hat JBoss BRMS uses hashing to extend Rete by optimizing the propagation from ObjectTypeNode to AlphaNode. Each time an AlphaNode is added to an ObjectTypeNode, it adds the literal value as a key to the HashMap with the AlphaNode as the value. When a new instance enters the ObjectType node, rather than propagating to each AlphaNode, it retrieves the correct AlphaNode from the HashMap. This avoids unnecessary literal checks.

When facts enter from one side, you may do a hash lookup returning potentially valid candidates (referred to as indexing). At any point a valid join is found, the Tuple joins with the Object (referred to as a partial match) and then propagates to the next node.

BetaNodes

BetaNodes are used to compare two objects and their fields. The objects may be of the same or different types.

Alpha Memory and Beta Memory

Alpha memory refers to the left input on a BetaNode. In Red Hat JBoss BRMS, this input remembers all incoming objects.

Beta memory is the term used to refer to the right input of a BetaNode. It remembers all incoming tuples.

Lookups with BetaNodes

When facts enter from one side, you can do a hash lookup returning potentially valid candidates (referred to as indexing). If a valid join is found, the Tuple joins with the Object (referred to as a partial match) and then propagates to the next node.

LeftInputNodeAdapters

A LeftInputNodeAdapter takes an Object as an input and propagates a single Object Tuple.

Terminal Nodes

Terminal nodes are used to indicate when a single rule matches all its conditions (that is, the rule has a full match). A rule with an OR conditional disjunctive connective results in a sub-rule generation for each possible logical branch. Because of this, one rule can have multiple terminal nodes.

Node Sharing

Node sharing is used to prevent redundancy. As many rules repeat the same patterns, node sharing allows users to collapse those patterns so that the patterns need not be reevaluated for every single instance.

The following rules share the first pattern but not the last:

rule
when
  Cheese($cheddar : name == "cheddar")
  $person: Person(favouriteCheese == $cheddar)
then
  System.out.println($person.getName() + "likes cheddar");
end

rule
when
  Cheese($cheddar : name == "cheddar")
  $person: Person(favouriteCheese == $cheddar)
then
  System.out.println($person.getName() + "likes cheddar");
end

rule
when
  Cheese($cheddar : name == "cheddar")
  $person : Person(favouriteCheese != $cheddar)
then
  System.out.println($person.getName() + " does not like cheddar");
end

rule
when
  Cheese($cheddar : name == "cheddar")
  $person : Person(favouriteCheese != $cheddar)
then
  System.out.println($person.getName() + " does not like cheddar");
end

The Rete network displayed below denotes that the alpha node is shared but the beta nodes are not. Each beta node has its own TerminalNode.

Figure 5.2. Node Sharing

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The Agenda is a Rete feature. During actions on the WorkingMemory, rules may become fully matched and eligible for execution. A single Working Memory Action can result in multiple eligible rules. When a rule is fully matched an Activation is created, referencing the rule and the matched facts, and placed onto the Agenda. The Agenda controls the execution order of these Activations using a Conflict Resolution strategy.

The engine cycles repeatedly through two phases:

The process repeats until the agenda is clear, in which case control returns to the calling application. When Working Memory Actions are taking place, no rules are being fired.

Conflict resolution is required when there are multiple rules on the agenda. As firing a rule may have side effects on the working memory, the rule engine needs to know in what order the rules should fire (for instance, firing ruleA may cause ruleB to be removed from the agenda).

Agenda groups are a way to partition rules on the agenda. At any one time, only one group has "focus" which means that activations for rules in that group only will take effect. You can also have rules with "auto focus" which means that the focus is taken for its agenda group when that rule’s conditions are true.

Agenda groups are known as "modules" in CLIPS terminology. Agenda groups provide a way to create a "flow" between grouped rules. You can switch the group which has focus either from within the rule engine, or via the API. If your rules have a clear need for multiple "phases" or "sequences" of processing, consider using agenda-groups for this purpose.

Each time setFocus() is called it pushes the specified Agenda Group onto a stack. When the focus group is empty it is popped from the stack and the focus group that is now on top evaluates. An Agenda Group can appear in multiple locations on the stack. The default Agenda Group is "MAIN", with all rules which do not specify an Agenda Group being in this group. It is also always the first group on the stack, given focus initially, by default.

The setFocus() method call looks like follows:

ksession.getAgenda().getAgendaGroup("Group A").setFocus();

ksession.getAgenda().getAgendaGroup("Group A").setFocus();

An activation group is a set of rules bound together by the same activation-group rule attribute. In this group only one rule can fire, and after that rule has fired all the other rules are cancelled from the agenda. The clear() method can be called at any time, which cancels all of the activations before one has had a chance to fire.

An activation group looks like follows:

ksession.getAgenda().getActivationGroup("Group B").clear();

ksession.getAgenda().getActivationGroup("Group B").clear();

The inference engine is the part of the Red Hat JBoss BRMS engine which matches production facts and data to rules. It is often called the brain of a Production Rules System as it is able to scale to a large number of rules and facts. It makes inferences based on its existing knowledge and performs the actions based on what it infers from the information.

The rules are stored in the production memory and the facts that the inference engine matches against, are stored in the working memory. Facts are asserted into the working memory where they may get modified or retracted. A system with a large number of rules and facts may result in many rules being true for the same fact assertion. Such conflicting rules are managed using a conflict resolution strategy. This strategy determines the order of execution of the rules by assigning a priority level to each rule.

Inferences can be forward chaining or backward chaining. In a forward chaining inference mechanism, when some data gets inserted into the working memory, the related rules are triggered and if the data satisfies the rule conditions, corresponding actions are taken. These actions may insert new data into the working memory and therefore trigger more rules and so on. Thus, the forward chaining inference is data driven. On the contrary, the backward chaining inference is goal driven. In this case, the system looks for a particular goal, which the engine tries to satisfy. If it cannot do so it searches for sub-goals, that is, conclusions that will complete part of the current goal. It continues this process until either the initial conclusion is satisfied or there are no more unsatisfied sub-goals. Correct use of inference can create agile and less error prone business rules, which are easier to maintain.

The following example illustrates how an inference is made about whether a person is eligible to have a bus pass based on the rule conditions. Here is a rule that provides the age policy for a person to hold a bus pass:

rule "Infer Adult"
when
  $p : Person(age >= 18)
then
  insert(new IsAdult($p))
end

rule "Infer Adult"
when
  $p : Person(age >= 18)
then
  insert(new IsAdult($p))
end

Based on this rule, a rule engine infers whether a person is an adult or a child and act on it. Every person who is 18 years or above will have an instance of IsAdult inserted for them in the working memory. This inferred relation of age and bus pass can be inferred in any rule, such as:

$p : Person()
IsAdult(person == $p)

$p : Person()
IsAdult(person == $p)

Figure 6.3. OpenOffice Screenshot

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In the above examples, the technical aspects of the decision table have been collapsed away (using a standard spreadsheet feature).

The rules start from row 17, with each row resulting in a rule. The conditions are in columns C, D, E, and the actions are off-screen. The values' meanings are indicated by the headers in Row 16. Column B is just a description.

When using decision tables, the spreadsheet searches for the RuleTable keyword to indicate the start of a rule table (both the starting row and column).

The RuleSet keyword indicates the name to be used in the rule package that will encompass all the rules. This name is optional, using a default, but it must have the RuleSet keyword in the cell immediately to the right.

There are two types of rectangular areas defining data that is used for generating a DRL file. One, marked by a cell labelled RuleSet, defines all DRL items except rules. The other one may occur repeatedly and is to the right and below a cell whose contents begin with RuleTable. These areas represent the actual decision tables, each area resulting in a set of rules of similar structure.

A Rule Set area may contain cell pairs, one below the RuleSet cell and containing a keyword designating the kind of value contained in the other one that follows in the same row.

The columns of a Rule Table area define patterns and constraints for the left hand sides of the rules derived from it, actions for the consequences of the rules, and the values of individual rule attributes. A Rule Table area should contain one or more columns, both for conditions and actions, and an arbitrary selection of columns for rule attributes, at most one column for each of these. The first four rows following the row with the cell marked with RuleTable are earmarked as header area, mostly used for the definition of code to construct the rules. It is any additional row below these four header rows that spawns another rule, with its data providing for variations in the code defined in the Rule Table header.

Entries in a Rule Set area may define DRL constructs (except rules), and specify rule attributes. While entries for constructs may be used repeatedly, each rule attribute may be given at most once, and it applies to all rules unless it is overruled by the same attribute being defined within the Rule Table area.

Entries must be given in a vertically stacked sequence of cell pairs. The first one contains a keyword and the one to its right the value. This sequence of cell pairs may be interrupted by blank rows or even a Rule Table, as long as the column marked by RuleSet is upheld as the one containing the keyword.

All Rule Tables begin with a cell containing RuleTable, optionally followed by a string within the same cell. The string is used as the initial part of the name for all rules derived from this Rule Table, with the row number appended for distinction. This automatic naming can be overridden by using a NAME column. All other cells defining rules of this Rule Table are below and to the right of this cell.

The next row after the RuleTable cell defines the column type. Each column results in a part of the condition or the consequence, or provides some rule attribute, the rule name or a comment. Each attribute column may be used at most once.

Given a column headed CONDITION, the cells in successive lines result in a conditional element.

Given a column headed ACTION, the cells in successive lines result in an action statement:

Given a column headed METADATA, the cells in successive lines result in a metadata annotation for the generated rules:

The SpreadsheetCompiler class is the main class used with API spreadsheet-based decision tables in the drools-decisiontables module. This class takes spreadsheets in various formats and generates rules in DRL.

The SpreadsheetCompiler can be used to generate partial rule files and assemble them into a complete rule package after the fact. This allows the separation of technical and non-technical aspects of the rules if needed.

In Excel, you can create lists of values. These can be stored in other worksheets to provide valid lists of values for cells.

When changes are being made to rules over time, older versions are archived. Some applications in Red Hat JBoss BRMS provide a limited ability to keep a history of changes, but it is recommended to use an alternative means of revision control.

A tabular data source can be used as a source of rule data. It can populate a template to generate many rules. This can allow both for more flexible spreadsheets, but also rules in existing databases for instance (at the cost of developing the template up front to generate the rules).

Here is an example of how you can configure the logging level on the package org.drools in your logback.xml file when you are using Logback:

<configuration>
  <logger name="org.drools" level="debug"/>
  ...
  ...
<configuration>

<configuration>
  <logger name="org.drools" level="debug"/>
  ...
  ...
<configuration>

Here is an example of how you can configure the logging level in your log4j.xml file when you are using Log4J:

<log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">
  <category name="org.drools">
    <priority value="debug" />
  </category>
  ...
</log4j:configuration>

<log4j:configuration xmlns:log4j="http://jakarta.apache.org/log4j/">
  <category name="org.drools">
    <priority value="debug" />
  </category>
  ...
</log4j:configuration>

To declare a fact type as an event, assign the @role metadata tag to the fact with the event parameter. The @role metadata tag can accept two possible values:

This example declares that a stock broker application’s StockTick fact type will be handled as an event:

import some.package.StockTick

declare StockTick
  @role( event )
end

import some.package.StockTick

declare StockTick
  @role( event )
end

Facts can also be declared inline. If StockTick was a fact type declared in the DRL instead of in a pre-existing class, the code would be as follows:

declare StockTick
  @role(event)

  datetime : java.util.Date
  symbol : String
  price : double
end

declare StockTick
  @role(event)

  datetime : java.util.Date
  symbol : String
  price : double
end

For more information about type declarations, see Section 8.9, “Type Declaration”.

Every event has associated metadata. Typically, the metadata is automatically added as each event is inserted into working memory. The metadata defaults can be changed on an event-type basis using the metadata tags:

The following examples assume the application domain model includes the following class:

/**
 * A class that represents a voice call in a Telecom domain model.
 */
public class VoiceCall {
  private String  originNumber;
  private String  destinationNumber;
  private Date    callDateTime;
  private long    callDuration;  // in milliseconds

  // Constructors, getters, and setters.
}

/**
 * A class that represents a voice call in a Telecom domain model.
 */
public class VoiceCall {
  private String  originNumber;
  private String  destinationNumber;
  private Date    callDateTime;
  private long    callDuration;  // in milliseconds

  // Constructors, getters, and setters.
}

Events have strong temporal constraints making it is necessary to use a reference clock. If a rule needs to determine the average price of a given stock over the last sixty minutes, it is necessary to compare the stock price event’s timestamp with the current time. The reference clock provides the current time.

Because the rules engine can simultaneously run an array of different scenarios that require different clocks, multiple clock implementations can be used by the engine.

Scenarios that require different clocks include the following:

JBoss BRMS Complex Event Processing comes equipped with two clock implementations:

For a list of Maven dependencies, see example Embedded jBPM Engine Dependencies. If you use Red Hat JBoss BRMS, see Embedded Drools Engine Dependencies.

Cloud mode is the default operating mode of Red Hat JBoss Business Rules Management System.

Running in Cloud mode, the engine applies a many-to-many pattern matching algorithm, which treats the events as an unordered cloud. Events still have timestamps, but there is no way for the rules engine running in Cloud mode to draw relevance from the timestamp because Cloud mode is unaware of the present time.

This mode uses the rules constraints to find the matching tuples, activate, and fire rules.

Cloud mode does not impose any kind of additional requirements on facts; however, because it has no concept of time, it cannot take advantage of temporal features such as sliding windows or automatic life-cycle management. In Cloud mode, it is necessary to explicitly retract events when they are no longer needed.

Certain requirements that are not imposed include the following:

Cloud mode can be specified either by setting a system property, using configuration property files, or using the API.

The API call follows:

import org.kie.api.conf.EventProcessingOption;
import org.kie.api.KieBaseConfiguration;
import org.kie.api.KieServices.Factory;

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption(EventProcessingOption.CLOUD);

import org.kie.api.conf.EventProcessingOption;
import org.kie.api.KieBaseConfiguration;
import org.kie.api.KieServices.Factory;

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption(EventProcessingOption.CLOUD);

The equivalent property follows:

drools.eventProcessingMode = cloud

drools.eventProcessingMode = cloud

For a list of Maven dependencies, see example Embedded jBPM Engine Dependencies. If you use Red Hat JBoss BRMS, see Embedded Drools Engine Dependencies.

Stream mode processes events chronologically as they are inserted into the rules engine. Stream mode uses a session clock that enables the rules engine to process events as they occur in time. The session clock enables processing events as they occur based on the age of the events. Stream mode also synchronizes streams of events (so events in different streams can be processed in chronological order), implements sliding windows of interest, and enables automatic life-cycle management.

The requirements for using stream mode are the following:

Stream mode can be enabled by setting a system property, using configuration property files, or using the API.

The API call follows:

import org.kie.api.conf.EventProcessingOption;
import org.kie.api.KieBaseConfiguration;
import org.kie.api.KieServices.Factory;

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption(EventProcessingOption.STREAM);

import org.kie.api.conf.EventProcessingOption;
import org.kie.api.KieBaseConfiguration;
import org.kie.api.KieServices.Factory;

KieBaseConfiguration config = KieServices.Factory.get().newKieBaseConfiguration();

config.setOption(EventProcessingOption.STREAM);

The equivalent property follows:

drools.eventProcessingMode = stream

drools.eventProcessingMode = stream

For a list of Maven dependencies, see example Embedded jBPM Engine Dependencies. If you use Red Hat JBoss BRMS, see Embedded Drools Engine Dependencies.

Entry points are declared implicitly by making direct use of them in rules. Referencing an entry point in a rule will make the engine, at compile time, identify and create the proper internal structures to support that entry point.

For example, a banking application that has transactions fed into the engine using streams could have one stream for all of the transactions executed at ATMs. A rule for this scenario could state, "A withdrawal is only allowed if the account balance is greater than the withdrawal amount the customer has requested."

rule "Authorize Withdraw"
when
  WithdrawRequest($ai : accountId, $am : amount) from entry-point "ATM Stream"
  CheckingAccount(accountId == $ai, balance > $am)
then
  // authorize withdraw
end

rule "Authorize Withdraw"
when
  WithdrawRequest($ai : accountId, $am : amount) from entry-point "ATM Stream"
  CheckingAccount(accountId == $ai, balance > $am)
then
  // authorize withdraw
end

When the engine compiles this rule, it will identify that the pattern is tied to the entry point ATM Stream. The engine will create all the necessary structures for the rule-base to support the ATM Stream, and this rule will only match WithdrawRequest events coming from the ATM Stream.

Note the ATM example rule joins the event (WithdrawalRequest) from the stream with a fact from the main working memory (CheckingAccount).

The banking application may have a second rule that states, "A fee of $2 must be applied to a withdraw request made using a branch teller."

rule "Apply Fee on Withdraws on Branches"
when
  WithdrawRequest($ai : accountId, processed == true) from entry-point "Branch Stream"
  CheckingAccount(accountId == $ai)
then
  // apply a $2 fee on the account
end

rule "Apply Fee on Withdraws on Branches"
when
  WithdrawRequest($ai : accountId, processed == true) from entry-point "Branch Stream"
  CheckingAccount(accountId == $ai)
then
  // apply a $2 fee on the account
end

This rule matches events of the same type (WithdrawRequest) as the example ATM rule but from a different stream. Events inserted into the ATM Stream will never match the pattern on the second rule, which is tied to the Branch Stream; accordingly, events inserted into the Branch Stream will never match the pattern on the example ATM rule, which is tied to the ATM Stream.

Declaring the stream in a rule states that the rule is only interested in events coming from that stream.

Events can be inserted manually into an entry point instead of directly into the working memory.

import org.kie.api.runtime.KieSession;
import org.kie.api.runtime.rule.EntryPoint;

// Create your rulebase and your session as usual:
KieSession session = ...

// Get a reference to the entry point:
EntryPoint atmStream = session.getEntryPoint("ATM Stream");

// ...and start inserting your facts into the entry point:
atmStream.insert(aWithdrawRequest);

import org.kie.api.runtime.KieSession;
import org.kie.api.runtime.rule.EntryPoint;

// Create your rulebase and your session as usual:
KieSession session = ...

// Get a reference to the entry point:
EntryPoint atmStream = session.getEntryPoint("ATM Stream");

// ...and start inserting your facts into the entry point:
atmStream.insert(aWithdrawRequest);

For a list of Maven dependencies, see example Embedded jBPM Engine Dependencies. If you use Red Hat JBoss BRMS, see Embedded Drools Engine Dependencies.

A negative pattern is concerned with conditions that are not met. Negative patterns make reasoning in the absence of events possible. For instance, a safety system could have a rule that states "If a fire is detected and the sprinkler is not activated, sound the alarm."

In Cloud mode, the engine assumes all facts (regular facts and events) are known in advance and evaluates negative patterns immediately.

rule "Sound the Alarm"
when
  $f : FireDetected()
  not(SprinklerActivated())
then
  // sound the alarm
end

rule "Sound the Alarm"
when
  $f : FireDetected()
  not(SprinklerActivated())
then
  // sound the alarm
end

An example in stream mode is displayed below. This rule keeps consistency when dealing with negative patterns and temporal constraints at the same time interval.

rule "Sound the Alarm"
  duration(10s)
when
  $f : FireDetected()
  not(SprinklerActivated(this after[0s,10s] $f))
then
  // sound the alarm
end

rule "Sound the Alarm"
  duration(10s)
when
  $f : FireDetected()
  not(SprinklerActivated(this after[0s,10s] $f))
then
  // sound the alarm
end

In stream mode, negative patterns with temporal constraints may force the engine to wait for a set time before activating a rule. A rule may be written for an alarm system that states, "If a fire is detected and the sprinkler is not activated after 10 seconds, sound the alarm." Unlike the previous stream mode example, this one does not require the user to calculate and write the duration parameter.

rule "Sound the Alarm"
when
  $f : FireDetected()
  not(SprinklerActivated(this after[0s,10s] $f))
then
  // sound the alarm
end

rule "Sound the Alarm"
when
  $f : FireDetected()
  not(SprinklerActivated(this after[0s,10s] $f))
then
  // sound the alarm
end

The rule depicted below expects one "Heartbeat" event to occur every 10 seconds; if not, the rule fires. What is special about this rule is that it uses the same type of object in the first pattern and in the negative pattern. The negative pattern has the temporal constraint to wait between 0 to 10 seconds before firing, and it excludes the Heartbeat bound to $h. Excluding the bound Heartbeat is important since the temporal constraint [0s, …​] does not exclude by itself the bound event $h from being matched again, thus preventing the rule to fire.

rule "Sound the Alarm"
when
  $h: Heartbeat() from entry-point "MonitoringStream"
  not(Heartbeat(this != $h, this after[0s,10s] $h) from entry-point "MonitoringStream")
then
  // sound the alarm
end

rule "Sound the Alarm"
when
  $h: Heartbeat() from entry-point "MonitoringStream"
  not(Heartbeat(this != $h, this after[0s,10s] $h) from entry-point "MonitoringStream")
then
  // sound the alarm
end

Complex Event Processing requires the rules engine to engage in temporal reasoning. Events have strong temporal constraints so it is vital the rules engine can determine and interpret an event’s temporal attributes, both as they relate to other events and the 'flow of time' as it appears to the rules engine. This makes it possible for rules to take time into account; for instance, a rule could state "Calculate the average price of a stock over the last 60 minutes."

JBoss BRMS Complex Event Processing implements the following temporal operators and their logical complements (negation):

The after operator correlates two events and matches when the temporal distance (the time between the two events) from the current event to the event being correlated falls into the distance range declared for the operator.

For example:

$eventA : EventA(this after[3m30s, 4m] $eventB)

$eventA : EventA(this after[3m30s, 4m] $eventB)

This pattern only matches if the temporal distance between the time when $eventB finished and the time when $eventA started is between the lower limit of three minutes and thirty seconds and the upper limit of four minutes.

This can also be represented as follows:

3m30s <= $eventA.startTimestamp - $eventB.endTimeStamp <= 4m

3m30s <= $eventA.startTimestamp - $eventB.endTimeStamp <= 4m

The after operator accepts one or two optional parameters:

The after operator also accepts negative temporal distances.

For example:

$eventA : EventA(this after[-3m30s, -2m] $eventB)

$eventA : EventA(this after[-3m30s, -2m] $eventB)

If the first value is greater than the second value, the engine will automatically reverse them.

The following two patterns are equivalent to each other:

$eventA : EventA(this after[-3m30s, -2m] $eventB)
$eventA : EventA(this after[-2m, -3m30s] $eventB)

$eventA : EventA(this after[-3m30s, -2m] $eventB)
$eventA : EventA(this after[-2m, -3m30s] $eventB)

The before operator correlates two events and matches when the temporal distance (time between the two events) from the event being correlated to the current event falls within the distance range declared for the operator.

For example:

$eventA : EventA(this before[3m30s, 4m] $eventB)

$eventA : EventA(this before[3m30s, 4m] $eventB)

This pattern only matches if the temporal distance between the time when $eventA finished and the time when $eventB started is between the lower limit of three minutes and thirty seconds and the upper limit of four minutes.

This can also be represented as follows:

3m30s <= $eventB.startTimestamp - $eventA.endTimeStamp <= 4m

3m30s <= $eventB.startTimestamp - $eventA.endTimeStamp <= 4m

The before operator accepts one or two optional parameters:

The before operator also accepts negative temporal distances.

For example:

$eventA : EventA(this before[-3m30s, -2m] $eventB)

$eventA : EventA(this before[-3m30s, -2m] $eventB)

If the first value is greater than the second value, the engine will automatically reverse them.

The following two patterns are equivalent to each other:

$eventA : EventA(this before[-3m30s, -2m] $eventB)
$eventA : EventA(this before[-2m, -3m30s] $eventB)

$eventA : EventA(this before[-3m30s, -2m] $eventB)
$eventA : EventA(this before[-2m, -3m30s] $eventB)

The coincides operator correlates two events and matches when both events happen at the same time.

For example:

$eventA : EventA(this coincides $eventB)

$eventA : EventA(this coincides $eventB)

This pattern only matches if both the start timestamps of $eventA and $eventB are identical and the end timestamps of both $eventA and $eventB are also identical.

The coincides operator accepts optional thresholds for the distance between the events' start times and the events' end times, so the events do not have to start at exactly the same time or end at exactly the same time, but they need to be within the provided thresholds.

The following rules apply when defining thresholds for the coincides operator:

For example:

$eventA : EventA(this coincides[15s, 10s] $eventB)

$eventA : EventA(this coincides[15s, 10s] $eventB)

This pattern will only match if the following conditions are met:

abs($eventA.startTimestamp - $eventB.startTimestamp) <= 15s
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 10s

abs($eventA.startTimestamp - $eventB.startTimestamp) <= 15s
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 10s

The during operator correlates two events and matches when the current event happens during the event being correlated.

For example:

$eventA : EventA(this during $eventB)

$eventA : EventA(this during $eventB)

This pattern only matches if $eventA starts after $eventB and ends before $eventB ends.

This can also be represented as follows:

$eventB.startTimestamp < $eventA.startTimestamp <= $eventA.endTimestamp < $eventB.endTimestamp

$eventB.startTimestamp < $eventA.startTimestamp <= $eventA.endTimestamp < $eventB.endTimestamp

The during operator accepts one, two, or four optional parameters:

The following rules apply when providing parameters for the during operator:

The finishes operator correlates two events and matches when the current event’s start timestamp post-dates the correlated event’s start timestamp and both events end simultaneously.

For example:

$eventA : EventA(this finishes $eventB)

$eventA : EventA(this finishes $eventB)

This pattern only matches if $eventA starts after $eventB starts and ends at the same time as $eventB ends.

This can be represented as follows:

$eventB.startTimestamp < $eventA.startTimestamp
&&
$eventA.endTimestamp == $eventB.endTimestamp

$eventB.startTimestamp < $eventA.startTimestamp
&&
$eventA.endTimestamp == $eventB.endTimestamp

The finishes operator accepts one optional parameter. If defined, the optional parameter sets the maximum time allowed between the end times of the two events.

For example:

$eventA : EventA(this finishes[5s] $eventB)

$eventA : EventA(this finishes[5s] $eventB)

This pattern matches if these conditions are met:

$eventB.startTimestamp < $eventA.startTimestamp
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 5s

$eventB.startTimestamp < $eventA.startTimestamp
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 5s

The finishedby operator correlates two events and matches when the current event’s start time predates the correlated event’s start time but both events end simultaneously. finishedby is the symmetrical opposite of the finishes operator.

For example:

$eventA : EventA(this finishedby $eventB)

$eventA : EventA(this finishedby $eventB)

This pattern only matches if $eventA starts before $eventB starts and ends at the same time as $eventB ends.

This can be represented as follows:

$eventA.startTimestamp < $eventB.startTimestamp
&&
$eventA.endTimestamp == $eventB.endTimestamp

$eventA.startTimestamp < $eventB.startTimestamp
&&
$eventA.endTimestamp == $eventB.endTimestamp

The finishedby operator accepts one optional parameter. If defined, the optional parameter sets the maximum time allowed between the end times of the two events.

$eventA : EventA(this finishedby[5s] $eventB)

$eventA : EventA(this finishedby[5s] $eventB)

This pattern matches if these conditions are met:

$eventA.startTimestamp < $eventB.startTimestamp
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 5s

$eventA.startTimestamp < $eventB.startTimestamp
&&
abs($eventA.endTimestamp - $eventB.endTimestamp) <= 5s

The includes operator examines two events and matches when the event being correlated happens during the current event. It is the symmetrical opposite of the during operator.

For example:

$eventA : EventA(this includes $eventB)

$eventA : EventA(this includes $eventB)

This pattern only matches if $eventB starts after $eventA and ends before $eventA ends.

This can be represented as follows:

$eventA.startTimestamp < $eventB.startTimestamp <= $eventB.endTimestamp < $eventA.endTimestamp

$eventA.startTimestamp < $eventB.startTimestamp <= $eventB.endTimestamp < $eventA.endTimestamp

The includes operator accepts 1, 2 or 4 optional parameters:

The meets operator correlates two events and matches when the current event ends at the same time as the correlated event starts.

For example:

$eventA : EventA(this meets $eventB)

$eventA : EventA(this meets $eventB)

This pattern matches if $eventA ends at the same time as $eventB starts.

This can be represented as follows:

abs($eventB.startTimestamp - $eventA.endTimestamp) == 0

abs($eventB.startTimestamp - $eventA.endTimestamp) == 0

The meets operator accepts one optional parameter. If defined, it determines the maximum time allowed between the end time of the current event and the start time of the correlated event.

For example:

$eventA : EventA(this meets[5s] $eventB)

$eventA : EventA(this meets[5s] $eventB)

This pattern matches if these conditions are met:

abs($eventB.startTimestamp - $eventA.endTimestamp) <= 5s

abs($eventB.startTimestamp - $eventA.endTimestamp) <= 5s

The metby operator correlates two events and matches when the current event starts at the same time as the correlated event ends.

For example:

$eventA : EventA(this metby $eventB)

$eventA : EventA(this metby $eventB)

This pattern matches if $eventA starts at the same time as $eventB ends.

This can be represented as follows:

abs($eventA.startTimestamp - $eventB.endTimestamp) == 0

abs($eventA.startTimestamp - $eventB.endTimestamp) == 0

The metby operator accepts one optional parameter. If defined, it sets the maximum distance between the end time of the correlated event and the start time of the current event.

For example:

$eventA : EventA(this metby[5s] $eventB)

$eventA : EventA(this metby[5s] $eventB)

This pattern matches if these conditions are met:

abs($eventA.startTimestamp - $eventB.endTimestamp) <= 5s

abs($eventA.startTimestamp - $eventB.endTimestamp) <= 5s

The overlaps operator correlates two events and matches when the current event starts before the correlated event starts and ends after the correlated event starts, but it ends before the correlated event ends.

For example:

$eventA : EventA(this overlaps $eventB)

$eventA : EventA(this overlaps $eventB)

This pattern matches if these conditions are met:

$eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp

$eventA.startTimestamp < $eventB.startTimestamp < $eventA.endTimestamp < $eventB.endTimestamp

The overlaps operator accepts one or two optional parameters:

The overlappedby operator correlates two events and matches when the correlated event starts before the current event, and the correlated event ends after the current event starts but before the current event ends.

For example:

$eventA : EventA(this overlappedby $eventB)

$eventA : EventA(this overlappedby $eventB)

This pattern matches if these conditions are met:

$eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp

$eventB.startTimestamp < $eventA.startTimestamp < $eventB.endTimestamp < $eventA.endTimestamp

The overlappedby operator accepts one or two optional parameters:

The starts operator correlates two events and matches when they start at the same time, but the current event ends before the correlated event ends.

For example:

$eventA : EventA(this starts $eventB)

$eventA : EventA(this starts $eventB)

This pattern matches if $eventA and $eventB start at the same time, and $eventA ends before $eventB ends.

This can be represented as follows:

$eventA.startTimestamp == $eventB.startTimestamp
&&
$eventA.endTimestamp < $eventB.endTimestamp

$eventA.startTimestamp == $eventB.startTimestamp
&&
$eventA.endTimestamp < $eventB.endTimestamp

The starts operator accepts one optional parameter. If defined, it determines the maximum distance between the start times of events in order for the operator to still match:

$eventA : EventA(this starts[5s] $eventB)

$eventA : EventA(this starts[5s] $eventB)

This pattern matches if these conditions are met:

abs($eventA.startTimestamp - $eventB.startTimestamp) <= 5s
&&
$eventA.endTimestamp < $eventB.endTimestamp

abs($eventA.startTimestamp - $eventB.startTimestamp) <= 5s
&&
$eventA.endTimestamp < $eventB.endTimestamp

The startedby operator correlates two events. It matches when both events start at the same time and the correlating event ends before the current event.

For example:

$eventA : EventA(this startedby $eventB)

$eventA : EventA(this startedby $eventB)

This pattern matches if $eventA and $eventB start at the same time, and $eventB ends before $eventA ends.

This can be represented as follows:

$eventA.startTimestamp == $eventB.startTimestamp
&&
$eventA.endTimestamp > $eventB.endTimestamp

$eventA.startTimestamp == $eventB.startTimestamp
&&
$eventA.endTimestamp > $eventB.endTimestamp

The startedby operator accepts one optional parameter. If defined, it sets the maximum distance between the start time of the two events in order for the operator to still match:

$eventA : EventA( this starts[5s] $eventB)

$eventA : EventA( this starts[5s] $eventB)

This pattern matches if these conditions are met:

abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s
&&
$eventA.endTimestamp > $eventB.endTimestamp

abs( $eventA.startTimestamp - $eventB.startTimestamp ) <= 5s
&&
$eventA.endTimestamp > $eventB.endTimestamp

Stream mode allows events to be matched over a sliding time window. A sliding window is a time period that stretches back in time from the present. For instance, a sliding window of two minutes includes any events that have occurred in the past two minutes. As events fall out of the sliding time window (in this case because they occurred more than two minutes ago), they will no longer match against rules using this particular sliding window.

For example:

StockTick() over window:time(2m)

StockTick() over window:time(2m)

JBoss BRMS Complex Event Processing uses the over keyword to associate windows with patterns.

Sliding time windows can also be used to calculate averages and over time. For instance, a rule could be written that states "If the average temperature reading for the last ten minutes goes above a certain point, sound the alarm."

rule "Sound the Alarm in Case Temperature Rises Above Threshold"
when
  TemperatureThreshold($max : max)
  Number(doubleValue > $max) from accumulate(
    SensorReading($temp : temperature) over window:time(10m),
    average($temp))
then
  // sound the alarm
end

rule "Sound the Alarm in Case Temperature Rises Above Threshold"
when
  TemperatureThreshold($max : max)
  Number(doubleValue > $max) from accumulate(
    SensorReading($temp : temperature) over window:time(10m),
    average($temp))
then
  // sound the alarm
end

The engine will automatically discard any SensorReading more than ten minutes old and keep re-calculating the average.

Similar to Time Windows, Sliding Length Windows work in the same manner; however, they consider events based on order of their insertion into the session instead of flow of time.

The pattern below demonstrates this order by only considering the last 10 RHT Stock Ticks independent of how old they are. Unlike the previous StockTick from the Sliding Time Windows pattern, this pattern uses window:length.

StockTick(company == "RHT") over window:length(10)

StockTick(company == "RHT") over window:length(10)

The example below portrays window length instead of window time; that is, it allows the user to sound an alarm in case the average temperature over the last 100 readings from a sensor is above the threshold value.

rule "Sound the Alarm in Case Temperature Rises Above Threshold"
when
  TemperatureThreshold($max : max)
  Number(doubleValue > $max) from accumulate(
    SensorReading($temp : temperature) over window:length(100),
    average($temp))
then
  // sound the alarm
end

rule "Sound the Alarm in Case Temperature Rises Above Threshold"
when
  TemperatureThreshold($max : max)
  Number(doubleValue > $max) from accumulate(
    SensorReading($temp : temperature) over window:length(100),
    average($temp))
then
  // sound the alarm
end

Explicit expiration is set with a declare statement and the metadata @expires tag.

For example:

declare StockTick
  @expires(30m)
end

declare StockTick
  @expires(30m)
end

Declaring expiration against an event-type will, in the above example StockTick events, remove any StockTick events from the session automatically after the defined expiration time if no rules still need the events.