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DEBS 2012 presentation:
A basic proactive model

Yagil Engel, Opher Etzion, Zohar Feldman

IBM 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_limit


When 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: time
of detection, expected time of crash, duration to end of the drill, available options

                                               4                                 © 2012 IBM Corporation
When is the crash expected?
                                                Temporal context: overlapping
We would like this pattern to generate a        sliding window of 10 minutes
derived event called “equipment crash”          from each measurement
whose 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 TIME
Online information:       pattern
Detection time, size                         INTERVAL BETWEEN NOW AND DRILL END
of interval, trend of                1
Temperature measurement
since start of drill

Prediction model is created
offline using regular
prediction 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 crash

Operating in low                      + low setup cost;
 pressure                             - harms productivity
                                      ? medium probability to prevent crash


Full                                  - 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 which
The event pattern                                                  minimizes the cost is
has been detected                                                  maintenance at time = 30
in Feb 8, 10:00
Time = 0


   2
                     Cost




                                                                            4
Normalizing all to
cost units –                                                       :Action
calculation of                                                     Schedule maintenance
expected cost                                                      for Feb 9, 16:00
distribution for
every 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




                                            Cost
Cost




                                                   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 scenarios
with 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 events

In event processing systems derived events are
VIRTUAL EVENTS that are assumed to happen
when created




In our model forecasted derived events are OBSERVALE
EVENTS that are assumed to happen in the future.

The actual occurrence of the event as well as the
occurrence time are uncertain and require the extension
of the event processing model with uncertainty
representation 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 action



15                                                   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 exit
Monitoring streams of events from         traffic congestion of certain      traffic lights durations and
sensor in highway and leading             size will occur in                 speed limit in highway
ways, from mobile devices, and            probability of 0.6                 segments
from 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-based
optimization)
(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/#comments

The scenario: PREPROCESS - Simulation-based optimization of scheduling and resource allocation
off-line for all surgeries planned for the next day
DETECT
Real-time tracking of everything: physicians, nurses, equipment; monitor of procedure duration and
status - using sensors, cameras - exploiting the "Internet of Things“
FORECAST
Determination of things already going wrong (not according to plan) and anticipation when the surgery
will end/resources will be used
ACT
Re-applying the simulation based optimization (this time online!) and get updated resource allocation
plan.

              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 by
event-based optimization)
 (Example 2: Freshdirect (reported by Timo Elliot, SAP
  =http://smartdatacollective.com/timoelliott/45868/2012-year-analytics-means-business?refnode_other_posts_by

The scenario:
PREPROCESS - Plan distribution of merchandise by trucks
DETECT
Real-time tracking of trucks
FORECAST
Determination that in the next hour deliveries planned will
be below target
ACT
The company applies its reserve trucks to replace trucks
that 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 the
further 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

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Debs 2012 basic proactive

  • 1. DEBS 2012 presentation: A basic proactive model Yagil Engel, Opher Etzion, Zohar Feldman IBM Haifa Research Lab © 2012 IBM Corporation
  • 2. 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
  • 3. 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
  • 4. 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_limit When 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: time of detection, expected time of crash, duration to end of the drill, available options 4 © 2012 IBM Corporation
  • 5. When is the crash expected? Temporal context: overlapping We would like this pattern to generate a sliding window of 10 minutes derived event called “equipment crash” from each measurement whose 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 TIME Online information: pattern Detection time, size INTERVAL BETWEEN NOW AND DRILL END of interval, trend of 1 Temperature measurement since start of drill Prediction model is created offline using regular prediction modeling. NOW Drill end 0 80 Feb 8, 10:00 Feb 11, 18:00 5 © 2012 IBM Corporation
  • 6. What are the possible actions? + low cost; Lubrication + does not harm productivity; - relative low probability to prevent crash Operating in low + low setup cost; pressure - harms productivity ? medium probability to prevent crash Full - 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
  • 7. Some concrete (simulated) results 3 1 The action which The event pattern minimizes the cost is has been detected maintenance at time = 30 in Feb 8, 10:00 Time = 0 2 Cost 4 Normalizing all to cost units – :Action calculation of Schedule maintenance expected cost for Feb 9, 16:00 distribution for every action was done Feb 8, 10:00 Feb 9, 16:00 Feb 11, 18:00 (Time =0) 7 © 2012 IBM Corporation
  • 8. Note that the decision is sensitive to timing of detection Cost Cost 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
  • 9. Some experimental results with various scenarios with 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
  • 10. 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
  • 11. 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
  • 12. The proactive use pattern 12 © 2012 IBM Corporation
  • 13. 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
  • 14. Forecasted derived events In event processing systems derived events are VIRTUAL EVENTS that are assumed to happen when created In our model forecasted derived events are OBSERVALE EVENTS that are assumed to happen in the future. The actual occurrence of the event as well as the occurrence time are uncertain and require the extension of the event processing model with uncertainty representation and handling 14 © 2012 IBM Corporation
  • 15. 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 action 15 15 © 2012 IBM Corporation
  • 16. 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
  • 17. 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 exit Monitoring streams of events from traffic congestion of certain traffic lights durations and sensor in highway and leading size will occur in speed limit in highway ways, from mobile devices, and probability of 0.6 segments from 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
  • 18. Scenario 3 - Surgery room scenario (decision by event-based optimization) (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/#comments The scenario: PREPROCESS - Simulation-based optimization of scheduling and resource allocation off-line for all surgeries planned for the next day DETECT Real-time tracking of everything: physicians, nurses, equipment; monitor of procedure duration and status - using sensors, cameras - exploiting the "Internet of Things“ FORECAST Determination of things already going wrong (not according to plan) and anticipation when the surgery will end/resources will be used ACT Re-applying the simulation based optimization (this time online!) and get updated resource allocation plan. 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
  • 19. Scenario 4: merchandise delivery scenario (decision by event-based optimization) (Example 2: Freshdirect (reported by Timo Elliot, SAP =http://smartdatacollective.com/timoelliott/45868/2012-year-analytics-means-business?refnode_other_posts_by The scenario: PREPROCESS - Plan distribution of merchandise by trucks DETECT Real-time tracking of trucks FORECAST Determination that in the next hour deliveries planned will be below target ACT The company applies its reserve trucks to replace trucks that 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
  • 20. Summary: what did we achieve? What are the further 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

Notas do Editor

  1. 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