Omg co p proactive computing oct 2010

Proactive event-driven computingOMG EP CoP: Event Processing Symposium: Capital markets, NYC, October 6th, 2010

Dr. Opher EtzionIBM Haifa Research [email protected]

OMG event processingSymposium, October 2010

© 2010 IBM Corporation2

Proactive Event-Driven Computing

Imagine that…

Your mortgage backed securities decisions are tuned based on The future effect of location-related events on the risk

Your are able to mitigate predicted events that would causeyour customer contact center to violate SLA




Outline of this talk The proactive event-driven computing idea and its relations to other technologies

Some building blocks

Some scenarios

The IBM Research project and your possible

involvement




The proactive event-driven computing idea and its relations to other technologies


Some scenarios


involvement




In June 2010 we presented six trends for event processing: Going from narrow to

wideGoing from monolithic to diversified

Going from proprietary to standard-based

Going from stand-alone to embedded

Going from reactive to proactive

Going from programmer centered to semi-technical developer

“Event processing – sevenYears from now”, Opher Etzion,OMG event processing virtual symposium, June 2010




Evolution of computing paradigms IBM Haifa Research Lab – Event Processing

IBM Haifa Research Lab – Event Processing © 2008 IBM Corporation

Resp

on

sive

reactiv

ePro

activ

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Proactive event-driven computing: the elevator speech

Based on occurrence of events, determine that a system has a high likelihood to get to an undesired state.

When detected – devise a plan to mitigate the undesired state, by eliminating it, or reducing its damage

IBM Haifa Research Lab – Event Processing

IBM Haifa Research Lab – Event Processing © 2008 IBM Corporation

TIME

now

Late

r

StableStates

Shift left

Action




The class of problems

The system state has a metric associated with it

The acceptable states are expressed as range on these metric.

The system can anticipate that it is going out of the acceptable states

The system finds a way either to get to acceptable state or closer

Characteristics Desired functionality




Gaps from event processing perspective

EventProcessing

Gap 1:

Operational vs.

Causality

based

When cell is added – add to total sales

When cell is deleted – delete from total sales

When cell is modified – delete the old value and add the new value to total sale

Analog: spreadsheet

Programming

Total Sales =Sum (all sale

cells) Gap 2:

Time and

Determinism Situation happens when detected (or at the end of some time window)

Situation will happenwithin 20-30 minutesThere is 0.4 probabilityof false positive

Gap 3:

Action:

Message Class Frequency (% of cases)

Cost (if delay > 15 mins)

Cost (if delay > 30 mins)

Cost (if delay> 60 mins)

1 5 200 500 1500 2 10 175 437 1312 3 15 150 375 1125 4 20 125 312 937 5 25 100 250 750 6 15 75 187 562 7 5 50 125 375 8 5 25 62 187




Gaps from BI Perspective

DataWarehouse Collect

Data Apply

(predictive) Analytics/Optimization

AnalyzeResults

Change strategySet policies

WatchEvent

Anticipate shortterm operationalproblem

Find best feasible alternative in giventimeframe

Decide& apply

Strategic vs. operational issuesBatch vs. time-constrained solutions

Proactive




The programming model gap






Some scenarios


involvement




What do we need in order to make it work?

Enhance the Event processing

Technology

Establish proactive action plan based oncausality network

Apply machine learning techniquesto assist in constructing proactive applications




Predict•Bayesian Network•Classifiers:

•Decision trees•Naïve Bayes•…

•Uncertain Rules•…

Act•Rules•State Machine•Temporal Decision Process•Optimization tools (black box)

Probabilisticevents

Probabilisticsituations

Analytics

EventsState

Actions

General Flow

ctions




Enhance the event

processing technology

ime interval

Events occur within an interval, possible in the future

PredictiveEPA

Predicted event with Certainty Measure




Enhance the event

processing technology

ProactiveAgents

Adding proactive agents, actions and feedback loop as part of the model




Establish proactive action plan based oncausality network

Causality network

AI planning techniques

Time constrained optimization




Pattern mining:

o Discovery of frequent event patterns

o Identification of event patterns that provide predictive information

Populate predictive models:

o Learn probabilities for causality models

o Learn transition probabilities

Interactive pattern discovery:

o Interleave data mining with data visualization

Apply machine learning techniquesto assist in constructing proactive applications






Some scenarios


involvement




Scenario: proactive management of mortgage-backed securities

“What is the loss of information? … It is very hard to determine the location of the risk, partly because of the chain of interlinked securities, which does not allow the final resting

place of the risk to be determined”

Gary Gorton, Yale University, “The panic of 2007”.




Scenario: proactive management of mortgage-backed securities

Location based patterns for real-estate value deterioration

Determine affected securities (causality network creates transparency)

Proactive Planning system

Risk policies

Decisions and actions

Feedback




Skill Based routingpolicies Event Processing

System

Approximate Dynamic Programming

Distribution Anomaly

Policy Adaptation

Routing tracing

Request traffic events

Shortage of certain resource

Employees

Related events

Events thattrigger manyRequest (outage, Bad weather)…

1

2

3

4

5

67

CRM scenario






Some scenarios


involvement




IBM Research The largest industrial organization ~ 3000 employees

six Nobel prizes (five in Physics and one in Economics)

six ACM Turing awards

Research in industry:o Close alignment with real-world business problems through research partnerships

and the “First of a Kind” program




IBM Research Worldwide

New in 2010: Brazil




First-of-a-Kind Program

Experimental technology-based solutions engagements

Testing tomorrow’s innovations on today’s business problems

Yielding prototype solutions across a range of industries

Creating valuable intellectual capital for IBM’s portfolio

IBM funds most of the project – some funding is required from the customer to ensure commitment




Client ValueEarly adopter market advantage

o Access to game changing technologies

o Test new approaches and thought leadership

First hand experience

o Emerging technologies

o Innovative solutions

o New business models

Access to world renowned researchers

o Skills and knowledge transfer

Provide input to IBM requirements process

o Shape and mold potential new offerings

Investment funding model

o Minimizes investment

o Enables experimentation and exploration




Deliverables Early thought leadership and experiences with new

technologies

Working prototype of an innovative solution not yet available in the marketplace

The know-how to improve a business process or solve a problem

Software components, methodologies and tools

Press & media coverage




Other models of working with IBM Research

Within government sponsored programs

Within larger IBM engagements

Joint studies with cost sharing




The proactive event-driven project Done in IBM Haifa Research Lab

Research partners from various countries

Current partner organizations so far in the areas of: healthcare, travel and logistics, chemical and petroleum

Financial services partners?

Documents

Omg co p proactive computing oct 2010