Internet Of Things: How Data Science Driven Software is Eating the Connected World

1© Copyright 2015 Pivotal. All rights reserved. 1© Copyright 2013 Pivotal. All rights reserved.
Internet of Things
How Data Science-Driven Software is Eating the
Connected World
Sarah Aerni, Principal Data Scientist
Pivotal
@itweetsarah
Hadoop Summit, San Jose, CA
June 10th

2© Copyright 2015 Pivotal. All rights reserved.
Our everyday devices are smart and talk to us

These devices are now talking to each other

Connected devices take action
to make daily life easier.
But what else?

How can IoT help prevent
accidents like the Macondo
Disaster ?

Gene Sequencing
Smart Grids
COST TO SEQUENCE
ONE GENOME
HAS FALLEN FROM
$100M IN 2001
TO $10K IN 2011
TO $1K IN 2014
READING SMART METERS
EVERY 15 MINUTES IS
3000X MORE
DATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS
250 MILLION
PHOTOS EACH DAY
In all industries billions of data points represent
opportunities for the Internet of Things
Oil Exploration
Video Surveillance
OIL RIGS GENERATE
25000
DATA POINTS
PER SECOND
Medical Imaging
Mobile Sensors

Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

How can data drive true, automated action?
How does this…

How does this…
…become this?

Ÿ  How is data collected?
Ÿ  Where is it stored and processed?
Ÿ  Is there real signal or just noise?
Ÿ  How can we build a predictive model?
Ÿ  When is the right time to take action?

Critical considerations for successful modeling
How to build a
predictive model at
scale
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling

How to build a
predictive model at
scale
Tradeoffs between
model accuracy
and timeliness
Use Cases
Oil Drilling Vaccine Manufacturing

How to build a
predictive model at
scale
Use Cases
Oil Drilling Vaccine Manufacturing
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness
Treating Patients

Use Cases
Oil Drilling Vaccine Manufacturing Treating Patients
How to build a
predictive model at
scale
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness

Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Ÿ  Oil & gas exploration and production activities generate large
amounts of data from sensors
Ÿ  What opportunities exist for data-driven approaches to improve
operations?
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

Data: The New Oil
California.
Credit: Stephen H.
Hickman, USGS
operations?
Drilling operations Predictive maintenance

Data: The New Oil
California.
Credit: Stephen H.
Hickman, USGS
operations?
Drilling operations
•  Predicting drill rate-of-penetration (ROP)
•  Motivation: Shorter time to oil production
lowers costs and increases production
•  Goals
–  Identify optimal parameters for rapid
drilling
–  Create an initial approach for drilling
–  Potential reduction in damage to
equipment
Predictive maintenance

Data: The New Oil
California.
Credit: Stephen H.
Hickman, USGS
operations?
Drilling operations
•  Predicting drill rate-of-penetration (ROP)
•  Motivation: Shorter time to oil production
lowers costs and increases production
•  Goals
–  Identify optimal parameters for rapid
drilling
–  Create an initial approach for drilling
–  Potential reduction in damage to
equipment
Predictive maintenance
•  Predict equipment function and failure
•  Motivation: Failure costs estimated at
$150,000/incident (billions annually)*
•  Goals
–  Early warning system
–  Insights into prominent features impacting
operation and failure
–  Reduction of non-productive drill time
–  Reduced incidents

How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling

Integrating
& Cleansing
Feature
Building
Modeling
Integrated Data
Primary data sources
Operator Data
( thousands of records )
•  Failure details
•  Component details
•  Drill Bit details
Drill Rig Sensor Data
( billions of records )
•  Rate of Penetration (ROP)
•  RPM
•  Weight on Bit (WOB)

Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Operator Data
•  RPM

Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Operator Data
•  RPM
Drill bit changes

Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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Operator Data
•  RPM

Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
Operator Data
•  RPM

Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
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Operator Data
•  RPM
•  Weight on Bit

Integrating
& Cleansing
WOB
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
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Operator Data
•  RPM

Integrating
& Cleansing
WOB
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
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Operator Data
•  RPM
A cleansing approach:
use average across a window

Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
Operator Data
•  RPM

Integrating
& Cleansing
Feature
Building
Modeling
BitpositionRPM
ROPWOB
•  A failure occurred at
the end of this run

Integrating
& Cleansing
Feature
Building
Modeling
the end of this run
•  Taking a window of
time prior to failure,
what features should
we extract (e.g.
variance of RPM,
max bit position
velocity)?
Bitposition

Integrating
& Cleansing
Feature
Building
Modeling
the end of this run
•  Taking a window of
time prior to failure,
what features should
we extract (e.g.
variance of RPM,
max bit position
velocity)?
RPM

Predict occurrence of equipment failure
in a chosen future time window
Predict remaining life of equipment
Predict Rate-of-Penetration
Integrating
& Cleansing
Feature
Building
Modeling

•  Logistic Regression
•  Elastic Net Regularized Regression (Binomial)
•  Support Vector Machines
Predict remaining life of equipment
Integrating
& Cleansing
Feature
Building
Modeling

Predict remaining life of equipment •  Cox Proportional Hazards Regression
Integrating
& Cleansing
Feature
Building
Modeling

Predict remaining life of equipment •  Cox Proportional Hazards Regression
•  Linear Regression
•  Elastic Net Regularized Regression (Gaussian)
Integrating
& Cleansing
Feature
Building
Modeling

Linear Regression: Streaming Algorithm
Ÿ  Finding linear
dependencies between
variables
–  ROP = c0+ WOB * cWOB
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)

variables
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)

variables
Ÿ  How to compute with a
single scan?
0
10
20
30
40
50
60
70
80
90
100
110
-10 15
Rateof
Penetration
(ROP)
Weight on
Bit (WOB)

Linear Regression: Parallel Computation

Segment 1 Segment 2

Linear regression on 10 million rows in seconds
0
50
100
150
200
0 50 100 150 200 250 300 350
6 Segments
12 Segments
18 Segments
24 Segments
Hellerstein, Joseph M., et al. "The MADlib analytics library: or MAD skills, the SQL." Proceedings of the VLDB Endowment 5.12 (2012): 1700-1711.
# independent variables
Executiontime(s)

BIG DATA MACHINE LEARNING IN SQL
http://madlib.net/
Predictive Modeling Library
Linear Systems
•  Sparse and Dense Solvers
Matrix Factorization
•  Single Value Decomposition (SVD)
•  Low-Rank
Generalized Linear Models
•  Linear Regression
•  Multinomial Logistic Regression
•  Cox Proportional Hazards
•  Regression
•  Elastic Net Regularization
•  Sandwich Estimators (Huber white,
clustered, marginal effects)
Machine Learning Algorithms
•  Principal Component Analysis (PCA)
•  Association Rules (Affinity Analysis, Market
Basket)
•  Topic Modeling (Parallel LDA)
•  Decision Trees
•  Ensemble Learners (Random Forests)
•  Conditional Random Field (CRF)
•  Clustering (K-means)
•  Cross Validation
Descriptive Statistics
Sketch-based Estimators
•  CountMin (Cormode-
Muthukrishnan)
•  FM (Flajolet-Martin)
•  MFV (Most Frequent
Values)
Correlation
Summary
Support Modules
Array Operations
Sparse Vectors
Random Sampling
Probability Functions
PMML Export

Critical Considerations for Successful Modeling
Use Cases
How to build a
predictive model at
scale
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness

Opportunities for
Data-Driven Decisions
in Pharma

Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product

A pipeline of sensors and opportunities for optimizing output

Automated raw
materials mixing

High-Content
Screens
Automated raw
materials mixing

Sensors
High-Content
Screens
Temp
Time
Absorbance
Elution volume
Velocity
Time
Automated raw
materials mixing

High-Content
Screens
Temp
Time
Absorbance
Elution volume
Velocity
Time
Automated raw
materials mixing
•  What opportunities exist for intervention, correction?
•  Which attributes should be used as features in a model?
•  When is the appropriate time to take action?

•  What opportunities exist for intervention, correction?
•  Which attributes should be used as features in a model?
•  When is the appropriate time to take action?
High-Content
Screens
Temp
Time
Absorbance
Elution volume
Velocity
Time
Automated raw
materials mixing
>6 months

Predicting vaccine potency using manufacturing data
Model generation and evaluation
True Potency
PredictedPotency
>6 months

True Potency
PredictedPotency
Data
Integration
Feature
Building
Modeling
>6 months

• Tracing product through pipeline
• Integrating manual and
automated data collection
• Missing data and outliers
Data
Integration
Feature
Building
Modeling
True Potency
PredictedPotency
>6 months

• Extract multiple features from
particular steps (duration, mean,
median, etc.)
• Considerations
•  Tunable vs. measures
•  Step in pipeline
Data
Integration
Feature
Building
Modeling
True Potency
PredictedPotency
>6 months
Temp
Time
Absorbance
Elution volume
Velocity
Time

True Potency
PredictedPotency
Data
Integration
Feature
Building
Modeling
>6 months
• Partial least squares
• Random forest
• Regularized regression

Interpreting the utility of a measure obtained during manufacturing based
on model outcomes
Building insights from models
Ÿ  Some features may
reveal tunable
parameters to alter
potency, others may
simply be markers
Ÿ  Opportunities to
provide real-time
feedback on data entry
errors and predicted
potency outcomes
Assayed value Duration of a step
Potency
Potency
Correlation=0.45 Correlation=0.38

Critical Considerations for Successful Modeling
Use Cases
How to build a
predictive model at
scale
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness

Beyond monitor alerts for crashing patients–Prediction means prevention
Powering the Connected Hospital






Clinical
Narratives

Use Cases in Healthcare
Building a case for leveraging data and data science within a hospital setting
SAMPLE USE CASES
Ÿ  Prevent unnecessary ED visits using air quality and patient histories to anticipate needed
prescription refills
Ÿ  Avoid keeping patients longer than needed due to poor coordination by predicting patient length-of-
stay leading to on-time planning
Ÿ  Early alerts for deteriorating patients to increase monitoring and improve outcomes
Ÿ  Prevent discharging patients prematurely through patient readmission models
Ÿ  Improve treatment pathways via mortality models for sepsis
INFLUENCE CHANGE by
finding drivers in the models
IMPROVE customer MODELS
using data-driven approaches
LEVERAGE previously
inaccessible DATA sources
Environment Approach Insights

Data & Platform Overview
Pivotal HD
Pivotal HAWQ
DATA PLATFORM
TOOLS
Ÿ  Data obtained from EPIC
Ÿ  Total unique encounters:
242,312,567
Ÿ  Total unique patient IDs:
11,195,934
Ÿ  Encounters from 6 healthcare
settings (including hospitals,
skilled nursing facilities,
ambulance and dialysis)
–  8 total hospitals used in LOS
–  2 regions
Ÿ  9 years of data
EPIC
DIAGNOSES PROCEDURES
LABORATORY
VALUES
MONITOR
FEEDS
BED
OCCUPANCY
ORDERS

Engineering over 300 features to improve models
Simple SQL enables rapid generation
of many creative features
•  Processing performed in the database
without having to move the data with
very simple SQL code
•  Reduced time to generate and
examine features enables rapid
iterations
•  Test hypotheses rapidly to examine if
features have an effect on LOS
Patient
Demographics
Patient Medical
History
Current
Admission
Prior Hospitalizations
ED
Stay
Outpatient
Utilization
Hospital
Attributes
Lab Results
(last 72 hrs)

Understanding drivers of length of stay through model interpretation
Model Results and Insights into Patient Outcomes
Data-driven approaches improved model fit by 66%, and predicts patient length of
stay in the hospital within 22 hours of true discharge (on average)
Patient history offers less information for AMI
Recent observations (from current admission), labs and
hospital features are more predictive of length of stay than
patient medical history
Current Admission
Lab
Medical History
Demographics
Hospital
None (complete model)
Variance Explained When Category Excluded
Patient
Demographics
Patient Medical
History
Current
Admissio
n
Prior Hospitalizations
ED
Stay
Outpatient
Utilization
Hospital
Attributes
Lab Results
(last 72 hrs)

Insight into hospital operations
Length of stay is not only biology. Admission Time, Day of
Week, hospital’s size and a hospital’s experience with
cardiology matter
Understanding drivers of length of stay through model interpretation
Model Results and Insights into Patient Outcomes
Data-driven approaches improved model fit by 66%, and predicts patient length of
stay in the hospital within 22 hours of true discharge (on average)
Patient history offers less information for AMI
Recent observations (from current admission), labs and
hospital features are more predictive of length of stay than
patient medical history
Current Admission
Lab
Medical History
Demographics
Hospital
None (complete model)
Variance Explained When Category Excluded Hour of the day
#ofAdmissions
Hour of the day
#ofDischarges

The Promise of Internet of Humans

Ÿ  Smart contact lenses and sensors to identify and
alert patients before catastrophic events (e.g.
blood sugar drop for diabetics)
Ÿ  Wearables to track patient disease progression
using objective measures
Ÿ  Track patient adherence
Ÿ  Detect disease outbreaks using sequencing in
sewer system samples
Ÿ  ECG monitoring on mobile phones for early
alerting of stroke

http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!

FOR FURTHER INFO, CHECKOUT…
•  Pivotal Data Product Info, Docs and Downloads @ http://pivotal.io/big-data
•  Pivotal Blog @ http://blog.pivotal.io
•  Pivotal Data Science Blog @ http://blog.pivotal.io/data-science-pivotal
•  Pivotal Academy @ https://pivotal.biglms.com
•  Or reach out to your local Pivotal Account Executive…

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