Omg co p   proactive computing oct 2010
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Proactive computing - presentation in the OMG event processing CoP in capital markets, NYC, October 6, 2010

Proactive computing - presentation in the OMG event processing CoP in capital markets, NYC, October 6, 2010

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Omg co p   proactive computing oct 2010 Omg co p proactive computing oct 2010 Presentation Transcript

  • Proactive event-driven computing OMG EP CoP: Event Processing Symposium: Capital markets, NYC, October 6 th , 2010 Dr. Opher Etzion IBM Haifa Research Lab [email_address]
  • 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 cause your 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 building blocks Some scenarios The IBM Research project and your possible involvement
  • In June 2010 we presented six trends for event processing: Going from narrow to wide Going 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 – seven Years from now”, Opher Etzion, OMG event processing virtual symposium, June 2010
  • Evolution of computing paradigms
  • 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
  • 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 Event Processing 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 happen within 20-30 minutes There is 0.4 probability of false positive Gap 3: Action:
  • Gaps from BI Perspective Data Warehouse Collect Data Apply (predictive) Analytics/ Optimization Analyze Results Change strategy Set policies Watch Event Anticipate short term operational problem Find best feasible alternative in given timeframe Decide & apply Strategic vs. operational issues Batch vs. time-constrained solutions Proactive
  • The programming model gap
  • 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
  • What do we need in order to make it work? Enhance the Event processing Technology Establish proactive action plan based on causality network Apply machine learning techniques to 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)
    Probabilistic events Probabilistic situations Analytics Events State Actions General Flow  ctions
  • Enhance the event processing technology  ime interval Events occur within an interval, possible in the future Predictive EPA Predicted event with Certainty Measure
  • Enhance the event processing technology Proactive Agents Adding proactive agents, actions and feedback loop as part of the model
  • Establish proactive action plan based on causality network Causality network AI planning techniques Time constrained optimization
    • Pattern mining:
      • Discovery of frequent event patterns
      • Identification of event patterns that provide predictive information
    • Populate predictive models:
      • Learn probabilities for causality models
      • Learn transition probabilities
    • Interactive pattern discovery:
      • Interleave data mining with data visualization
    Apply machine learning techniques to assist in constructing proactive applications
  • 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
  • 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
  • CRM scenario
  • 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
  • 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:
      • 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 Value
    • Early adopter market advantage
      • Access to game changing technologies
      • Test new approaches and thought leadership
    • First hand experience
      • Emerging technologies
      • Innovative solutions
      • New business models
    • Access to world renowned researchers
      • Skills and knowledge transfer
    • Provide input to IBM requirements process
      • Shape and mold potential new offerings
    • Investment funding model
      • Minimizes investment
      • 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?