debs 2012 basic proactive

© 2012 IBM Corporation© 2012 IBM Corporation

DEBS 2012 presentation: A basic proactive model

Yagil Engel, Opher Etzion, Zohar Feldman

IBM Haifa Research Lab

© 2012 IBM Corporation2

What are we trying to achieve?

The basic proactive model is applicable for certain types of applications, it is a first phase in building a library of proactive models

“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 the right action at the right time to gain optimal value for a quantitative metric, given an anticipated unplanned event .


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 space of possibilities is too large and it is not feasible to compute all states offline

The timing of detection and of action can change the results – decision and action have real-time constraints

The anticipated event is uncertain, and its occurrence time is also uncertain – the prediction contains occurrence time expectancy over a relevant time interval


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 one: surface temperature

The monitored pattern is “surface temperature is consistently at least 4% more than upper limit for a period of 10 minutes”

Temporal context: overlapping sliding window of 10 minutes from each measurementSegmentation context: surface

Pattern: For each measurementtemperature > 1.04 *

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


When is the crash expected? Temporal context: overlapping sliding window of 10 minutes from each measurementSegmentation context: surface

Pattern: For each measurementtemperature > 1.04 * surface.upper_limit

We would like this pattern to generate a derived event called “equipment crash” whose occurrence time is in the future

The timing of the crash event is uncertain, it is expressed as EXPECTANCY DISTRIBUTION OVER THE TIME INTERVAL BETWEEN NOW AND DRILL END

Online information:Detection time, sizeof interval, trend of Temperature measurementsince start of drill

Prediction model is createdoffline using regularprediction modeling.

Anevent

pattern

NOW Drill end

1

0

Feb 8, 10:00

80

Feb 11, 18:00


What are the possible actions?

Lubrication+ low cost; + does not harm productivity;- relative low probability to prevent crash

Operating in low pressure

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

Full maintenance

- high cost - productivity is substantially harmed+ high probability to prevent crash

Questions

1. What is the action that will maximize the utility?

2. When is the best time to activate this activity?

A function of the costs and durations of actions, impact

on the target event


Some concrete (simulated) results

Feb 8, 10:00 Feb 11, 18:00

1

The event pattern has been detected in Feb 8, 10:00

Time = 0

2

Normalizing all to cost units – calculation of expected cost distribution for every action was done

)Time =0(

4

The action which minimizes the cost is maintenance at time = 30

3

Feb 9, 16:00

Action:

Schedule maintenance for Feb 9, 16:00

Cost


Note that the decision is sensitive to timing of detection

If the detection is done close to beginning of drilling session–

Feb 1, 08:00, then it is better to do lubrication now

If the detection is done closer to the end of the drilling session - to beginning of drilling session – Feb 9, 16:00 then it is better to go to low pressure mode after 30 hours

)Feb 10, 20:00(

Cost

Cost


Some experimental results with various scenarios with variance in temperature trends

Y axis = temperature percentage above normal

Myopic = execute the decision now

In scenarios 1 and 3 there are significant improvements when timing of action is also a decision


Let’s view some of the characteristics of this example

PropertyOur approach Alternatives

What triggers actionable decision?

Predicted eventRequest, periodic calculation

How is the target event predicted?

Event pattern determines the event, timing and attributes of events by predicting model using event patterns results as input

Pre-calculated, by applying predictive model on request

When is the prediction done?

When the pattern is matchedIn off-line, on request, as part of periodic calculation

When is the predicted event expected to occur?

Over an interval with expectancy distribution

In fixed-time point, somewhere in an interval

How is the decision done?

By a decision process that takes the time distribution of predicted event , costs and duration of actions, expected impacts of actions

By using pre-determined rules, by using pre-determined scoring model, by simulation

When is the action scheduled to be activated?

In the time on which the expected utility is

optimized – part of the decision process.

Immediately when model is applied, by manual decision.


Some alternative and complementary approaches

Alternative approach

Pros Cons

Off-line optimization

Generic, good results – can complementour solution as the “typical case”

Low level abstractions, not suitable for real-time

Using rule-based decision

Intuitive, suitable when trade-off is not involved or trivial – can complement our solution to fine-tune the action

Decisions are designed by user, not optimized, not applicable for large number of occurrences.

Sequential decision models (e.g. MDP)

Optimized, considering all possible statesComplementary – adapted version

Complicated, applicable to small amount of states

Reinforcement learning

General, continuously adapted, does not require much modeling

Results may not be optimized, requires significant amount of historical data


The proactive use pattern


What are the additions to the event processing model?

Forecasted derived events with uncertainty

Introducing proactive agent to the event processing

network


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


Action*{Ce, Ca(t), d, e’(T)}Action*{Ce, Ca(t), d, e’(T)}

ContextContext

ProducerProducer

ActuatorActuator

Event Processing Agent

Event Processing Agent

ConsumerConsumer

Event Type{name, attribute*}

ProactiveEPA

ProactiveEPA

Forecasted Event Type{name, attribute*, e(T)}

Action {t, parameter*}

Time to take the action

Time distribution of the occurrence of the event until time T - (life expectancy)

Ce – cost of the event if this action is takenCa(t) – cost of the action if taken based on the time it is takend – duration of the actione’(T) – time distribution of the event if action is taken

15

The enhanced event processing model with proactive agents


Monitoring of location, time, and magnitude of earthquake, and reported damages

Based on seismic sensors and citizen reports

Monitoring of location, time, and magnitude of earthquake, and reported damages

Based on seismic sensors and citizen reports

Forecasting that within the next 3 hours there will be a a potential damage in a certain location based on an event causality model

Forecasting that within the next 3 hours there will be a a potential damage in a certain location based on an event causality model

Taking proactive actions in notifying and performing actions such as: close roads, stop trains, turn off gas and water supply, evacuate people…

Taking proactive actions in notifying and performing actions such as: close roads, stop trains, turn off gas and water supply, evacuate people…

detect forecast decide act

Real-time decisions about steps and protocols to be followed

Real-time decisions about steps and protocols to be followed

Scenario 1: Disaster management scenario

Scenario properties:Big variance in disaster related developing scenario. Type of decisions vary among casesAspects: life saving, economic, environmental


Scenario 2 - Road management scenario

Detect

Monitoring streams of events from sensor in highway and leading ways, from mobile devices, and from accidents reports

Monitoring streams of events from sensor in highway and leading ways, from mobile devices, and from accidents reports

Forecast Act(proactive)

Forecasting that at some point in 10-15 minutes a traffic congestion of certain size will occur in probability of 0.6

Forecasting that at some point in 10-15 minutes a traffic congestion of certain size will occur in probability of 0.6

Taking proactive actions in setting up entry and exit traffic lights durations and speed limit in highway segments

Taking proactive actions in setting up entry and exit traffic lights durations and speed limit in highway segments

Decide(RT)

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 constraintsAspects: economic, quality of life, environmental


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

DETECTReal-time tracking of everything: physicians, nurses, equipment; monitor of procedure duration and status - using sensors, cameras - exploiting the "Internet of Things“

FORECASTDetermination of things already going wrong (not according to plan) and anticipation when the surgery will end/resources will be used

ACTRe-applying the simulation based optimization (this time online!) and get updated resource allocation plan.

Scenario 3 - Surgery room scenario (decision by event-based optimization)

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


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?ref=node_other_posts_by

The scenario:PREPROCESS - Plan distribution of merchandise by trucks

DETECTReal-time tracking of trucks

FORECASTDetermination that in the next hour deliveries planned will be below target

ACTThe 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


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

debs 2012 basic proactive

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