PANEL Streams vs Rules vs Subscriptions: System and Language Issues. The Case for Rules. Paul Vincent TIBCO Software Inc.

Transcription

1 PANEL Streams vs Rules vs Subscriptions: System and Language Issues The Case for Rules Paul Vincent TIBCO Software Inc.

2 Rules, rules, everywhere Data aquisition Data processing Workflow Data relationships Data constraints Business calculations Process exceptions Business decisions Compliance regulations Etc etc Business logic 2

3 C/EP, a technology 3

4 C/EP = various technology components Relationship rules State transition rules Pattern matching rules Reaction / decision rules 4

5 Rule = <conditions> result Logic: <conditions> inferred fact Constraint: <conditions> constraint satisfaction fact Data relationship: <ownership> relationship fact Production: <conditions> action ECA: <event conditions><conditions> action State model: <state><event><conditions> new state Query: <conditions> resultset CEP : <event><state><conditions> new state 5

6 Rules are across IT Solutions TIBCO infrastructure CEP Services Rules about events and deducing information and consequences BPM Workflow Services Rules representing flow decisions, process exceptions, declarative processes, decision services User Interface Services Rules for data validation, data constraints SOA Services Rules representing message switching, data transforms BAM Services Rules checking business activities, and related responses MDM Services Rules for Master Data Management transformations Enterprise Service Bus Business Logic Rules representing business decisions Custom Java etc 6

7 Rule types: per RuleML as used in CEP query Rule rule IntegrityRule DerivationRule ReactionRule ProductionRule TransformationRule SQL: 1999 Assertion SQL: 1999 View ECAPRule ECARule InferenceRule {OR} ProdeduralRule OCL 2.0 Invariant SQL: 1999 Trigger XSL 1.0 Rule Oracle 10g SQL View XSB 2.6 Prolog Rule MS Outlook 6 Rule ILOG JRule Jess 3.4 Rule BlazeAdvisorRule rule function query + callback rule with event declaration Rule classification per Gerd Wagner, RuleML 7

8 EPTS Glossary: on Rules Constraint (also event pattern constraint): A Boolean condition that must be satisfied by the events observed in a system. Examples: Event processing rules may be prescribed in many different ways, A service level agreement limiting the time taken to complete a mortgage transaction from the time including an application by is finite received. state machines, Rule (in event processing): A prescribed method for processing events. Examples: activity diagrams, Java code, SQL code, ECA (event-condition-action) rules or reactive rules that are triggered by event patterns. Whenever three timeouts have happened send an alert to the network manager. If more than ten shopping carts have been active for more than five minutes then activate the website reaction time monitor and display an amber alert on the dashboard. Whenever IBM trades 2% above its 1 hour VWAP and then within 15 minutes trades 5 points below then buy 1000 shares IBM. Notes: Event processing rules may be prescribed in many different ways, including by finite state machines, activity diagrams, Java code, SQL code, ECA (event-condition-action) rules or reactive rules that are triggered by event patterns. Event pattern triggered reactive rule: a rule that prescribes actions to be taken whenever an instance of a given event pattern is detected. 8

9 C/EP = a process, development to deployment Relationships, properties, inheritance rules Subscriptions Behavioural rules Set definition rules Rule execution algorithms 9

10 Advanced Patterns & Event Behaviors Many EP apps fit the standard CEP patterns: Filter interesting rules Detect predefined patterns / state changes Update data / invoke processes and services based on business rules and high level events Advanced EP: Exploiting inferencing to maximise understanding of EP chains Apply interesting statistical functions to event data to detect new / complex trends Apply different algorithms to event data Modify parameters used in other rules ( metarules ) Filter Join Decide React 10

11 Example: Rete-driven Production Rules Scope / declaration Classes / Events relevant for the rule Conditions Declarative Filters on declarations Performance Joins across declarations Actions Scalable... What to do for each combination Inference = of declaration knowledge instances that satisfies the conditions Inference = Event-by-event knowledge-based but not set semantics Filter Join Decide React 11

12 Rule Engine Fundamentals: Initial Stage Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory Rule R1 Scope Conditions Actions C1 C1.Pj=True Ruleset Scope defines relevant classes and events 12

13 Rule Engine Fundamentals: Simple Operation Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 A2 B1 Rule R1 Scope Conditions Actions C1 C1.Pj=True Ruleset At runtime, a Rule definition acts like a class, with tuplebased instances R1 tuples are: {A1} {A2} No conditions Both {A1} & {A2} can fire => A1.Pj = True and A2.Pj = True 13

14 Rule Engine Fundamentals: Filters Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 Pi=T A2 Pi=F B1 Rule R1 Scope Conditions Actions C1 C1.Pi C1.Pj=True Ruleset Conditions act as WHERE clauses to filter tuples R1 tuples are: {A1} {A2} Condition result is {A1} => A1.Pj = True 14

15 Rule Engine Fundamentals: Tuples and Joins Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 Pi=T A2 Pi=F B1 Pk=T Rule R1 Scope Conditions Actions C1, C2 C1.Pi C1.Pj=True Ruleset Tuple dimension is based on # of declarations R1 tuples are: {A1, B1} {A2, B1} Condition result is {A1, B1} => A1.Pj = True 15

16 Rule Engine Fundamentals: Cross Product Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 Pi=T A2 Pi=F B1 Pk=T B2 Pk=T 16 Rule R1 Scope Conditions Actions C1, C2 C1.Pi C2.Pk C1.Pj=True Ruleset # of instances in a class impacts tuple numbers (and action performance is linear) R1 tuples are: {A1, B1}, {A2, B1}, {A1, B2}, {A2, B2} Condition result is {A1, B1}, {A1, B2} => A1.Pj = True

17 Rule Engine Fundamentals: Rule Cycle Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 Pi=T A2 Pi=F B1 Pk=T B2 Pk=T Rule R1 Scope Conditions Actions C1, C2 C1.Pi C2.Pk A2.Pi=True Ruleset Rule execution causes change in data states R1 tuples are: {A1, B1}, {A2, B1}, {A1, B2}, {A2, B2} Condition leaves {A1, B1}, {A1, B2} => A2.Pi is set to True 17

18 Rule Engine Fundamentals: Inferencing Event E1 Class C1 Properties Pi, Pj Class C2 Property Pk Working Memory A1 Pi=T A2 Pi=F B1 Pk=T B2 Pk=T Rule R1 Scope Conditions Actions C1, C2 C1.Pi C2.Pk A2.Pi=True Ruleset Rule engine cycles until all valid tuples are processed R1 tuples are: now: {A1, B1}, {A2, B1}, {A1, {A2, B2}, Condition {A2, B2} Condition result is leaves {A2, B1}, {A1, {A2, B1}, B2} {A1, B2} => A2.Pi = True (again) 18

19 TBD: Advanced Rules Parameterised Rules Metarules to adjust these parameters Uncertainty Scoring Generating rules Machine learning Predictive Analytics Reasoning + Ontologies Other types of rules Constraint Logic Programming Goal-driven rules General high-level Business Rules / constraints / strategies to events and rules (ie processes / process generation) 19

20 Summary All EPLs can be considered as consisting of abstract rules Various models and execution paradigms exist with different optimisations for different use cases Work on abstract CEP pattern list (and best model, execution paradigms) still TBD etc etc 20