Exciting New Areas for Rules Paul Vincent, TIBCO Softwarefiles.meetup.com/987383/RuleML2010-TIBCO-WebRuleResearchMeets... · Potential for Intelligent Conversation Aggregated Product

Exciting New Areas for Rules

Paul Vincent, TIBCO Software

© Copyright 2000-2010 TIBCO Software Inc.

Paul Vincent, TIBCO Software

Rules vs “The Semantic Web”

Declarative Behaviors

Models Languages

Logic


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

Decisions

Current Practices


Models Languages

Logic

Data

Ontologies


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

DecisionsActivity/”Process”

StateEvent Rules

Production /

Reaction Rules

Interesting Area #1


Models Languages

Logic


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

Decisions

Complex Event

Processing

CEP-driven Business Processes

eventsEvent Transport

& Distribution


Pattern

Detection

Event & State store

Decisions Reactions

Complex Event Processing

Roles:

Sense and Respond

© Copyright 2000-2010 TIBCO Software Inc.6

Sense and Respond

Track and Trace

Situational Awareness

Generalised Role for Event Processing

complex events

Asynchronous

Events

Continuous &

parallel event

pattern detection

simple events

Declarative

processing


BPM:

business

process

SOA:

IT

services

CEP: complex

event

processing

State store

Multiple event types

/ channels

Orchestrated /

procedural processing

Event Sources

eventsPublish-subscribe /

queue-based:

JMS

-TIBCO EMS -

event

channels


Others

TCP-IP

SOAP over

HTTP

RVPattern

Detection

Event & State store

-TIBCO EMS -

IBM MQ etc

Decisions Reactions

Event Pattern Detection and

Processing


& Distribution


Pattern

Detection

Event & State store

Decisions Reactions

State

Transitions

Continuous

Queries

Pattern RegEx

Event Rules

Event Pattern State Management, FT,

DR


& Distribution


Pattern

Detection

In-memory

Decisions Reactions

Distributed Cache

DBMS

Decisions and Rules against Event

Patterns


& DistributionManaged

Decision

Models


Pattern

Detection

Event & State store

Decisions Reactions

Event Rules

Inference Rules

Models

Decisions and Rules against Event

Patterns


& Distribution

Services


Pattern

Detection

Event & State store

Decisions Reactions

Fact Updates

New Events

Processes

Services


Operational Intelligence

in Retail Banking

The Retail Challenge

Small Windows of Opportunity for Selling SuccessfullyP

rop

en

sity

to

Bu

y

Highest Chance to Sell

SuccessfullyThe Longer The Delay in

Selling, The Lesser the

Chance to Sell Successfully

The Challenges


Time Between Desire to Buy and Sales Call

Pro

pe

nsi

ty t

o B

uy

Propensity to Buy

The ChallengesTo Identify the Right Product(s) to Sell at the

Right Moment to the Right Customer(s)

Po

ten

tia

l fo

r In

tell

ige

nt

Co

nve

rsa

tio

n

Aggregated

Product Holdings, &

BalancesChanges in holdings,

Balance triggers,

Bureau data

Customer

Provided Data Renewal dates,

Financial priorities,

other holdingsProduct

Holdings &

Balances:Open & closed,

last x balancesProduct Usage

& events

Futu

re F

ocu

s

Non-core personal

Recognition of Customer Contact/Transaction Events as Important Assets to Effective Campaign

Execution and Management

Touchpoint

EventsATM/ AVR Transactions,

Web usage

Need: Event-Driven Marketing Rules


Po

ten

tia

l fo

r In

tell

ige

nt

Co

nve

rsa

tio

n

Part of Application

Ongoing usage

Aggregation

Customer Data capture

Contact data:Title, forenames, surname, premise #,

address, postcode,DoB, e.mail address,

Preferred name, Correspondence address,

Telephone # (home, work, mobile), GPP

markers (for e.mail,

telephone and post).

Core personalGender, marital status,

# dependants,

employment status,

occupational status,

residential status

Tax number

& eventsTransactions,

Transaction types,

time since last

Contact histories:Contact events,

Credit Events,

campaign codes Hyg

ien

e

Customer

Satisfaction Levels and issues

*Data we capture as part of our daily business

Breadth of Customer Understanding

Non-core personal

details needs to be

managed by either

the individual

customer contact or

back end admin

systems/ processes.

Selling + Offering the Right Products + Services

at the Right Time with Real-Time Response

Credit Card Purchase of Airline Ticket

at 1230pm Today via Agent/Airlines

Up sell Travel Insurance via Call Centre at 1pm Same

Day

Example Real-time Rules


Change of Home Address at Call Centre Up sell New Home Insurance/Personal Loans at Call

Centre at the Same Time

IPO Subscription of Certain Amount Up sell to Turn the Unallocated Amt to Fix Deposit if,

Say, More than > S$200K

Event-driven, declarative, business rules approach

used in conjunction with customer knowledge

and driving Business Processes

#Campaign and Offers pa 500 Campaigns + 4 M Offers

#Target Customers >4M

Transaction Volume >240 Msgs/Sec

Revenue Growth Campaign Performance

1. Campaign 1: 235% Over Campaign Revenue Target

Real Rule ROI


235% Over Campaign Revenue Target

2. Campaign 2:160% Over Campaign Revenue Target

3. Campaign 3270% Over on Additional Spending Target

4. Campaign 4250% Over Campaign Revenue Target

Time to Market per Marketing Campaign

Concept to Execution:

Before: 3 – 6 Months

After: 7 – 8 Days

Process Awareness in Shipping


Situation Awareness of Business Assets

Shipment container track and trace:

Monitoring shipment milestones and invoking

appropriate processes


54 million active milestones for

shipments in-situ at any one time

Original Business Solution

1. Custom J2EE application

Project Metric

Analysts + Developers 8

Elapsed Time 8 months



Implementation 100 milestones

Productivity 64 person mths per 100

milestones

De facto Business Solution = BPM +

SOA2. BPM + SOA

Project Metric

Analysts + Developers 3

+ unknown # vendor


+ unknown # vendor

consultants




milestones

Current Solution = CEP

3. Event-driven rules

Project Metric

Analysts + Developers 3 + 1 vendor consultant






milestones

Comparison of Approaches

Approach Cost Cost vs Java

Java 64 1x

BPM for events 400 6x

Event-driven rules 28 overall 0.5x

Event-driven rules 12 best case 0.2x



used in conjunction with, driving,

Business Processes

Event-driven rules 12 best case 0.2x

Shipping milestones covered

100+

Number of shipments in process

8K

Average shipment lifecycle / containers per shipment

100 Days

2 containers per shipment

Real Rule ROI


shipment2 containers per shipment

Average milestones achieved per container / events processed

64 milestones

60 business events per sec

Solution results 1 - Business agility: can now add new milestones without IT in less time

2 – Business IT agility: declarative model makes updates easier

3 – Solution is high performance and scales to business


RFID-BASED MANUFACTURING

EFFICIENCY

Manufacturing is Labor Intense

100% testing?

Test results data vs Error

lifecycle


testing?

Manual, expensive

tests

Not all process steps are necessary…

• Reliability depends on known event-

based conditions

• Monitoring these events can give good

information on the likelihood of a need

for a test

• Example: after a restart production

station A is


station A is

75% reliable for first 4 uses

100% reliable for the next 11 days

80% reliable thereafter


used in conjunction with production process

analytics and knowledge

Manufacturing tasks carried out per day

24K

Number of process steps Several 100s

Deployment Today: France2010-2011: Worldwide rollout

Solution results 30% reduction in manual test costs

Real Rule ROI


Solution results 30% reduction in manual test costs

“Significant reduction” in overall manufacturing costs

Event

Future trends in business processing

Decision Action


Adaptive Case Management

BPMS

BRMS

Events

Services

Example: Complex Fulfillment

TIBCO Active Fulfillment TIBCO Active Catalog

Fulfillment events

+ exception events


BPM Platform: workflow processes

CEP Platform: rules, states, goals

plan-based provisioning MDM / Content

Interesting Area #2


Models Languages

Logic

Web-based /

Cloud-based

Knowledge

Modelling


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

Decisions

Modelling

Interesting Area #3


Models Languages

Logic

Machine Learning of

Classifications and

Ontologies


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

Decisions

Lots of Potential Synergies!


Models Languages

Logic

Machine Learning of

Classifications and

Ontologies

Web-based /

Cloud-based

Knowledge

Modelling


Production Rules

Queries etc

Event Rules

Inference Rules

Processes etc

Decisions

Complex Event

Processing

Modelling

Future for Semantic Web?

• Distributed knowledge and information

being commercialised as Data Grids, Compute

Grids, Cloud Computing

• Universal models and rules, standardisation


• Universal models and rules, standardisation

being commercialised as event pattern

languages (EMP?), production rules

(DMN/PRR/RIF), processes (BPMN), etc

• Next generation research IT being used (RDF,

OWL, SPARQL etc) in complex use cases

Documents

Exciting New Areas for Rules Paul Vincent, TIBCO Softwarefiles.meetup.com/987383/RuleML2010-TIBCO-WebRuleResearchMeets... · Potential for Intelligent Conversation Aggregated Product