Debs 2012 basic proactive
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DEBS 2012 presentation of the paper on basic proactive by Yagil Engel, Opher Etzion and Zohar Feldman

DEBS 2012 presentation of the paper on basic proactive by Yagil Engel, Opher Etzion and Zohar Feldman

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  • Proactive event driven applications follow a 4 stage pattern: Detect phase – we monitor and detect interesting events. In our case we get the location, time, and magnitude of the earthquake from seismic sensors and damages from reports of citizens (uploaded questionnaires on-line to the web). Forecast phase – based on the detected events and causality models we calculate the potential loss and deformation Decide phase – based on the forecasted events we decide in real-time the steps and protocols to be followed Act phase – upon on the decision some actions are taken. Note that these can be automatic, e.g. broadcasting alerts, stopping a train, closing a bridge, closing a nuclear plant, or recommended actions like send troops or equipment to certain area, closing of places and evacuation of people

Debs 2012 basic proactive Debs 2012 basic proactive Presentation Transcript

  • DEBS 2012 presentation:A basic proactive modelYagil Engel, Opher Etzion, Zohar FeldmanIBM Haifa Research Lab © 2012 IBM Corporation
  • What are we trying to achieve? “Rapid business, economic, social, and political changes are leading organizations to shift their thinking from reactive (sense and response) to proactive (seek, model, and adapt) in order to detect opportunity and threat events that could affect their business”. Gartner #208030, December 2010 The goal is to apply theright action at theright time to gain optimal value for a quantitative metric, given an .anticipated unplanned event The basic proactive model is applicable for certain types of applications, it is a first phase in building a library of proactive models 2 © 2012 IBM Corporation
  • Some features of the problems we are approaching There is a quantitatively significant value of mitigating/preventing anticipated event. the goal is to optimize this value The way to anticipate the event is by itself event-driven (causality relations among events, or situation driven activation of prediction model), the events may have some uncertainty associated with them The anticipated event is uncertain, and its occurrence time is also uncertain – the prediction contains occurrence time expectancy over a relevant time interval The timing of detection and of action can change the results – decision and action have real-time constraints The space of possibilities is too large and it is not feasible to compute all states offline 3 © 2012 IBM Corporation
  • Let’s start with a simple story An oil drilling session started in February 1st 6:00 and is scheduled to last until February 11th 18:00 There are variety of sensors checking various factors that might cause equipment break – for the story we’ll concentrate on a single Temporal context: overlapping one: surface temperature sliding window of 10 minutes from each measurement Segmentation context: surface The monitored pattern is “surface temperature is consistently at least Pattern: For each measurement 4% more than upper limit for a temperature > 1.04 * period of 10 minutes” surface.upper_limitWhen detecting this pattern we are interested in knowing: when a crash is expected(and how likely is it)? what is the best action from cost/benefit perspective given: timeof detection, expected time of crash, duration to end of the drill, available options 4 © 2012 IBM Corporation
  • When is the crash expected? Temporal context: overlappingWe would like this pattern to generate a sliding window of 10 minutesderived event called “equipment crash” from each measurementwhose occurrence time is in the future Segmentation context: surface Pattern: For each measurement temperature > 1.04 * surface.upper_limit An The timing of the crash event is uncertain, it is expressed event as EXPECTANCY DISTRIBUTION OVER THE TIMEOnline information: patternDetection time, size INTERVAL BETWEEN NOW AND DRILL ENDof interval, trend of 1Temperature measurementsince start of drillPrediction model is createdoffline using regularprediction modeling. NOW Drill end 0 80 Feb 8, 10:00 Feb 11, 18:00 5 © 2012 IBM Corporation
  • What are the possible actions? + low cost;Lubrication + does not harm productivity; - relative low probability to prevent crashOperating in low + low setup cost; pressure - harms productivity ? medium probability to prevent crashFull - high cost maintenance - productivity is substantially harmed + high probability to prevent crash A function of the costs and Questions durations of actions, impact on the target event 2. What is the action that will maximize the utility? 3. When is the best time to activate this activity? 6 © 2012 IBM Corporation
  • Some concrete (simulated) results 3 1 The action whichThe event pattern minimizes the cost ishas been detected maintenance at time = 30in Feb 8, 10:00Time = 0 2 Cost 4Normalizing all tocost units – :Actioncalculation of Schedule maintenanceexpected cost for Feb 9, 16:00distribution forevery action was done Feb 8, 10:00 Feb 9, 16:00 Feb 11, 18:00(Time =0) 7 © 2012 IBM Corporation
  • Note that the decision is sensitive to timing of detection CostCost If the detection is done closer If the detection is done close to to the end of the drilling session – beginning of drilling session - to beginning of drilling session – Feb 9, 16:00 then it Feb 1, 08:00, then it is better to is better to go to low pressure do lubrication now mode after 30 hours (Feb 10, 20:00) 8 © 2012 IBM Corporation
  • Some experimental results with various scenarioswith variance in temperature trends Myopic = execute the decision now In scenarios 1 and 3 there are Y axis = temperature percentage significant improvements when timing above normal of action is also a decision 9 © 2012 IBM Corporation
  • Let’s view some of the characteristics of this example Property Our approach Alternatives What triggers actionable Predicted event Request, periodic calculation decision? How is the target event Event pattern determines the event, Pre-calculated, by applying predictive predicted? timing and attributes of events by model on request predicting model using event patterns results as input When is the prediction When the pattern is matched In off-line, on request, as part of done? periodic calculation When is the predicted Over an interval with expectancy In fixed-time point, somewhere in an event expected to occur? distribution interval How is the decision By a decision process that takes the time By using pre-determined rules, by done? distribution of predicted event , costs and using pre-determined scoring model, duration of actions, expected impacts of by simulation actions When is the action In the time on which the expected utility Immediately when model is applied, scheduled to be is by manual decision. activated? optimized – part of the decision process. 10 © 2012 IBM Corporation
  • Some alternative and complementary approaches Alternative Pros Cons approach Off-line Generic, good results – can complement Low level abstractions, not suitable optimization our solution as the “typical case” for real-time Using rule-based Intuitive, suitable when trade-off is not Decisions are designed by user, decision involved or trivial – can complement our not optimized, not applicable for solution to fine-tune the action large number of occurrences. Sequential Optimized, considering all possible states Complicated, applicable to small decision models Complementary – adapted version amount of states (e.g. MDP) Reinforcement General, continuously adapted, does not Results may not be optimized, learning require much modeling requires significant amount of historical data 11 © 2012 IBM Corporation
  • The proactive use pattern 12 © 2012 IBM Corporation
  • What are the additions to the event processing model? Forecasted derived events with uncertainty Introducing proactive agent to the event processing network 13 © 2012 IBM Corporation
  • Forecasted derived eventsIn event processing systems derived events areVIRTUAL EVENTS that are assumed to happenwhen createdIn our model forecasted derived events are OBSERVALEEVENTS that are assumed to happen in the future.The actual occurrence of the event as well as theoccurrence time are uncertain and require the extensionof the event processing model with uncertaintyrepresentation and handling 14 © 2012 IBM Corporation
  • The enhanced event processing model with proactive agents Producer Consumer Context Event Type {name, attribute*} Event Forecasted Event Type Processing {name, attribute*, e(T)} Agent Time distribution of the occurrence of the event until time T - (life expectancy) Ce – cost of the event if this action is taken Proactive Ca(t) – cost of the action if EPA Actuator taken based on the time it is Action* taken Action {Ce, Ca(t), d, e’(T)} d – duration of the action {t, parameter*} e’(T) – time distribution of the event if action is taken Time to take the action15 15 © 2012 IBM Corporation
  • Scenario 1: Disaster management scenario detect forecast decide act Monitoring of Forecasting that Real-time Taking proactive location, time, within the next 3 decisions actions in notifying and magnitude of hours there will about and performing earthquake, and be a a potential steps and actions such as: reported damage in a protocols close roads, stop damages certain location to be trains, turn off gas based on an followed and water supply, Based on seismic event causality evacuate people… sensors and model citizen reports Scenario properties: Big variance in disaster related developing scenario. Type of decisions vary among cases Aspects: life saving, economic, environmental 16 © 2012 IBM Corporation
  • Scenario 2 - Road management scenario Detect Forecast Decide Act (RT) (proactive) Forecasting that at some Taking proactive actions in point in 10-15 minutes a setting up entry and exitMonitoring streams of events from traffic congestion of certain traffic lights durations andsensor in highway and leading size will occur in speed limit in highwayways, from mobile devices, and probability of 0.6 segmentsfrom accidents reports Scenario properties: Traffic can have chaotic behavior. Amount of possible solutions is very large and requires optimization based on the current observations under strict time constraints Aspects: economic, quality of life, environmental 17 © 2012 IBM Corporation
  • Scenario 3 - Surgery room scenario (decision by event-basedoptimization)(Example 1: Intelligent business operation in surgery rooms (reported by Jim Sinur, Gartner http://blogs.gartner.com/jim_sinur/2012/01/10/success-snippet-intelligent-business-operations/#commentsThe scenario: PREPROCESS - Simulation-based optimization of scheduling and resource allocationoff-line for all surgeries planned for the next dayDETECTReal-time tracking of everything: physicians, nurses, equipment; monitor of procedure duration andstatus - using sensors, cameras - exploiting the "Internet of Things“FORECASTDetermination of things already going wrong (not according to plan) and anticipation when the surgerywill end/resources will be usedACTRe-applying the simulation based optimization (this time online!) and get updated resource allocationplan. Scenario properties: Large variance in behavior of surgeries. There is a need to anticipate and schedule resources (rooms, physicians, equipment) Aspects: life threat, quality of life, economic 18 © 2012 IBM Corporation
  • Scenario 4: merchandise delivery scenario (decision byevent-based optimization) (Example 2: Freshdirect (reported by Timo Elliot, SAP =http://smartdatacollective.com/timoelliott/45868/2012-year-analytics-means-business?refnode_other_posts_byThe scenario:PREPROCESS - Plan distribution of merchandise by trucksDETECTReal-time tracking of trucksFORECASTDetermination that in the next hour deliveries planned willbe below targetACTThe company applies its reserve trucks to replace trucksthat are behind their schedule and re-plan Scenario properties: Large variance in travel time, especially in urban areas. Substantially reduce late delivery. Aspects: economic, reputation 19 © 2012 IBM Corporation
  • Summary: what did we achieve? What are thefurther challenges? 1. The basic proactive model is a feasibility demonstration point for the proactive event-driven paradigm 2. The model built is applicable for a set of applications with specific characteristics There are a lot of challenges: Real-time optimization models for other cases Forecasting models Consumability by users Scalability issues 20 © 2012 IBM Corporation