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
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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
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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)
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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
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13. What are the additions to the event processing model?
Forecasted derived events with
uncertainty
Introducing proactive agent
to the event processing
network
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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