Proactive eth talk

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Proactive event-driven computing
talk in ETH, March 2012

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Proactive eth talk

  1. 1. Proactive event-driven computingTowards Proactive Event-Driven ComputingTalk in ETH – March 2012 Opher Etzion (opher@il.ibm.com) IBM Haifa Research Lab © 2011 IBM Corporation
  2. 2. Proactive event-driven computing(The source code movie (spoiler The hero of the story is sent to the occupy the Body of a dead person during the last 8 minutes of His life, trying to find out who put dirty bomb inside -A train so he‘ll be stopped from doing the next attack In an unexpected turn of events he succeeds to Eliminate the attack and change the past IBM Haifa Research Lab 2 © 2012 IBM Corporation
  3. 3. Proactive event-driven computingThe proactive event-driven principle Forecast Proactive action Real-time decision Detect now time IBM Haifa Research Lab 3 © 2012 IBM Corporation
  4. 4. Proactive event-driven computingProactive traffic management systemIBM Haifa Research Lab 4 © 2012 IBM Corporation
  5. 5. Proactive event-driven computingThe proactive pattern Forecast Detect Decide Act (RT) (proactive) Taking proactive actions in setting up entry and exit traffic lights durations and Forecasting that at some speed limit in highway point in 15 minute a traffic segments congestion of certain size will occur in probability of Monitoring streams of events 0.6 from sensor in highway and leading ways, from mobile devices, and from accidents reportsIBM Haifa Research Lab 5 © 2012 IBM Corporation
  6. 6. Proactive event-driven computingProactive energy use for home consumers/producers IBM Haifa Research Lab 6 © 2012 IBM Corporation
  7. 7. Proactive event-driven computingThe proactive pattern Forecast Detect Decide Act (RT) (proactive) Using RT optimization – schedule appliances use and apply through actuators Forecasting that in the morning hours the household will not produce any energy and the power grid’s price Monitoring sun, wind and will be high demand on power gridIBM Haifa Research Lab 7 © 2012 IBM Corporation
  8. 8. Proactive event-driven computingProactive post-earthquake disaster management system IBM Haifa Research Lab 8 © 2012 IBM Corporation
  9. 9. Proactive event-driven computingThe proactive pattern Forecast Detect Decide Act (RT) (proactive) Taking proactive actions in notifying and performing actions like – close roads, Forecasting that at some reduce speed of trains, turn point in the next hour there off gas and water supply… is going to be a a potential damage in a certain location Monitoring earthquake, spread by sensors, and citizen reportsIBM Haifa Research Lab 9 © 2012 IBM Corporation
  10. 10. Proactive event-driven computing The evolution towards proactive computing Typically people employ computing in responsive The initiative remains in way: the person makes human hands; decisions and the computer most persons are not assists in data, knowledge, proactive by nature advice The initiative moves to Recently, there is more the computer; employment of computers reactions to events that in reactive way: events already occurred drive decisions (Detect- Derive-Decide-Do) The initiative moves to the computer; The vision is to move to proactive computing: (Detect-Derive-Predict- X actions to events before they occur Decide now-Do)IBM Haifa Research Lab 10 © 2012 IBM Corporation
  11. 11. Proactive event-driven computingWhy it is difficult to create proactive?solutions nowA way of thinking Multiple skills are needed Incompatible programming model of the moving parts, and gaps in each of them within the current product Predictive analytics Decision models Optimization Event ProcessingIBM Haifa Research Lab 11 © 2012 IBM Corporation
  12. 12. Proactive event-driven computingProactive computing as cultural change The culture in many organizations and personal behavior advocates a routine behavior governed by fixed set of rules Many people are deterred from ad-hoc behavior even if it has relative benefit in specific case and prefer statistical .metrics Current analytics tools are geared towards improving the ”fixed set of ”rules Proactive thinking is different – it provides exception behavior to mitigate or eliminate problems when current rules will not work IBM Haifa Research Lab 12 © 2012 IBM Corporation
  13. 13. Proactive event-driven computingThe Proactive pillars Proactive event-driven computing Integrative platform and validation Scalable platform Event processing Event recognition Event-based Human computer foundations and forecasting optimization interaction uncertain event adaptive real events, future recognition, time human events, expert optimization for interaction in correctness forecasting proactive proactive issues ,models decisions systems goal driven supervised learning Paradigm: methodology, seamless programming modelIBM Haifa Research Lab 13 © 2012 IBM Corporation
  14. 14. Proactive event-driven computingEvent-flow programming model: the EPN Agent 1 Agent 2 Event Event Producer 1 Consumer 1 State Event Consumer 2 Agent 3 Event Channel Producer 2 Event Consumer 3IBM Haifa Research Lab 14 © 2012 IBM Corporation
  15. 15. Proactive event-driven computing PRA can be Context defined per context segment, and receive events only from e1 d3 EPAs in the sameProducer EPA EPA context PRA can send events to d1 d4 EPAs, e.g., e3 ”emergency Messages to and ”generator fix from EPAs are )potentially A1 uncertain( events, EPA PRA OK with present or d4 Actuator future time d2 interval State Actuator may respond e2 immediately, or sendProducer EPA acknowledgement via an event Enrich from dB State Consumer EPAs may need to consult the current state of the dB PRA IBM Haifa Research Lab 15 © 2012 IBM Corporation
  16. 16. Proactive event-driven computing(Proactive Agent (PRA PRA Input: forecasted events + state information Output: Action – recommendation, activation, command to actuator Process: real-time decision making Real time decision making Spectrum from Trivial: decision tree Basic: basic conditions - MDP Advanced: simulation based optimization, advanced modelingIBM Haifa Research Lab 16 © 2012 IBM Corporation
  17. 17. Proactive event-driven computingEnhancing the current event processing technology ,Events may be uncertain: uncertainty about their occurrence occurrence time, and any of their attribute values; furthermore there may be uncertainty about relation between derived event and Situation, and propagation of uncertain values to derived eventsDerived event may occur in the future– (using predictive models)Running future time window in the presentFurthermore the semantics of derived eventChanges from virtual event to raw event Applying event processing abstractions to states – and use hybrid model IBM Haifa Research Lab 17 © 2012 IBM Corporation
  18. 18. Proactive event-driven computing By 2015, 80% of all available data will be uncertain By 2015 the number of networked devices will be double the entire global population. All 9000 sensor data has uncertainty. 8000 100Global Data Volume in Exabytes 90 The total number of social media 7000 accounts exceeds the entire global Aggregate Uncertainty % 80 population. This data is highly uncertain 6000 in both its expression and content. 70 ) gs 5000 s or hin 60 ns fT Data quality solutions exist for Se o 4000 50 et rn enterprise data like customer, te (In 3000 40 product, and address data, but this is only a fraction of the ia ) M ed d text 2000 30 total enterprise data. i al a n S ,oc audio 20 eo P 1000 (vid VoI 10 0 Enterprise Data Multiple sources: IDC,Cisco 2005 2010 2015 IBM Haifa Research Lab 18 © 2012 IBM Corporation
  19. 19. Proactive event-driven computing“The dimensions of “BIG DATA Data has to be processed in higher Velocity Data has high variability: poly- structured. Many sources: sensors, social media, multi- media… Volumes of data are constantly growing Veracity: Data has inherent uncertainty associated with it IBM Haifa Research Lab 19 © 2012 IBM Corporation
  20. 20. Proactive event-driven computingUncertainty aspects Meta-data representation Real-time decision under of uncertainty uncertainty Removal of uncertainty Propagation of uncertainty By Thresholds Bayesian Nets By Robust determination Semantic propagation Monte-Carlo methodsIBM Haifa Research Lab 20 © 2012 IBM Corporation
  21. 21. Proactive event-driven computingAdding canonic representation for:uncertainty handling Uncertain about the level of Uncertain whether an causality between a car reported event has occurred heading towards highway (e.g. accident) and a car getting into the highway Uncertain about the accuracy of a sensor input: count of Uncertain what really cars, velocity of cars… happened. What is the type and magnitude of the accident (vehicles involved, casualties) Uncertain about the validity The pattern: more of a forecasting pattern than 100 cars approach an area Uncertain when an event within 5 minutes after occurred (will occur): timing an accident derives a of forecasted congestion congestion forecasting Uncertain where an event occurred (will occur): location of forecasted Uncertain about the quality of congestion the decision about traffic lights settingIBM Haifa Research Lab 21 © 2012 IBM Corporation
  22. 22. Proactive event-driven computingReal-time decision under uncertainty Robust RT Optimization Stochastic RT Optimization Simulation-based Stochastic RT optimization RT optimization Simulation- base RT Simulation- optimization base RT optimizationIBM Haifa Research Lab 22 © 2012 IBM Corporation
  23. 23. Proactive event-driven computingLearning patterns and causalities Event Patterns This is a direction to reduce the complexity of application development Pattern and causality acquisition “There are challenges in doing it – since “detected situations are “inferred events“ and may not be reflected in past eventsIBM Haifa Research Lab 23 © 2012 IBM Corporation
  24. 24. Proactive event-driven computingSummary Proactive event driven computing is a new paradigm with potential big impact on society as well as future IT There is an ecosystem of external collaborators mainly working on proposed EU projectThe aim is to combine scienceand engineering to create ageneric software platformIBM Haifa Research Lab 24 © 2012 IBM Corporation

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