business impact from iot? just add data science
TRANSCRIPT
1 © Copyright 2015 Pivotal. All rights reserved. 1 © Copyright 2013 Pivotal. All rights reserved.
Business impact from IoT? Just add data science
Sarah Aerni, Principal Data Scientist Pivotal @itweetsarah Strata + Hadoop World, New York September 30th
2 © Copyright 2015 Pivotal. All rights reserved.
Our everyday devices are smart and talk to us
3 © Copyright 2015 Pivotal. All rights reserved.
These devices are now talking to each other
4 © Copyright 2015 Pivotal. All rights reserved.
How can connected devices in our home be smart enough to
make daily life easier?
5 © Copyright 2015 Pivotal. All rights reserved.
How can we know a tree has fallen on a power line before
the residents complain?
6 © Copyright 2015 Pivotal. All rights reserved.
How can we use data to help prevent
accidents like the Macondo Disaster ?
7 © Copyright 2015 Pivotal. All rights reserved.
How does this…
8 © Copyright 2015 Pivotal. All rights reserved.
How does this… …become this?
9 © Copyright 2015 Pivotal. All rights reserved.
How does this… …become this?
By recognizing this
10 © Copyright 2015 Pivotal. All rights reserved.
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
11 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Sensors & Actuators
12 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Sensors & Actuators
Data Lake
13 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Problem Formulation
Data Science for Building Models
Sensors & Actuators
Data Lake
14 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Problem Formulation
Data Step
Data Science for Building Models
Sensors & Actuators
Data Lake
15 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Problem Formulation
Modeling Step
Data Step
Data Science for Building Models
Sensors & Actuators
Data Lake
16 © Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and techniques
Problem Formulation
Modeling Step
Data Step Application Step
Data Science for Building Models
Sensors & Actuators
Data Lake
17 © Copyright 2015 Pivotal. All rights reserved.
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
18 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Vaccine Manufacturing Oil Drilling
19 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Derive insight from models to change
processes
Tradeoffs between model accuracy and timeliness
Vaccine Manufacturing Oil Drilling
20 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Derive insight from models to change
processes
Tradeoffs between model accuracy and timeliness
Vaccine Manufacturing Oil Drilling Vaccine Manufacturing
21 © Copyright 2015 Pivotal. All rights reserved.
Opportunities for Data-Driven Decisions in Pharma
22 © Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
23 © Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Sensors Te
mp
Time
Abs
orba
nce
Elution volume
Velo
city
Time
24 © Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Tem
p
Time
Abs
orba
nce
Elution volume
Velo
city
Time
• What opportunities exist for intervention, correction? • Which attributes should be used as features in a model? • When is the appropriate time to take action?
25 © Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Tem
p
Time
Abs
orba
nce
Elution volume
Velo
city
Time
• What opportunities exist for intervention, correction? • Which attributes should be used as features in a model? • When is the appropriate time to take action?
>6 months
26 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?
True Potency
Pre
dict
ed P
oten
cy
Input materials Mix Incubate Filter Centrifuge Final Product
27 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
20
df$ts_utc
df$w
ob
True Potency
Pre
dict
ed P
oten
cy
28 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
20
df$ts_utc
df$w
ob
• Deriving signal noisy sensor data requires data cleansing
29 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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• Deriving signal noisy sensor data requires data cleansing
30 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
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• Deriving signal noisy sensor data requires data cleansing
31 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
20
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A cleansing approach: use average across a window
• Deriving signal noisy sensor data requires data cleansing
• Window functions in SQL allow us to perform smoothing seamlessly, at-scale
32 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
20
df$ts_utc
df$w
ob
• Deriving signal noisy sensor data requires data cleansing
• Window functions in SQL allow us to perform smoothing seamlessly, at-scale
33 © Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models? Te
mpe
ratu
re
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
1015
20
df$ts_utc
df$w
ob
• Deriving signal noisy sensor data requires data cleansing
• Window functions in SQL allow us to perform smoothing seamlessly, at-scale
• Test many hypotheses in parallel to examine if features have an effect on potency
34 © Copyright 2015 Pivotal. All rights reserved.
Interpreting the utility of a measure obtained during manufacturing based on model outcomes
Building insights from models
� Some features may reveal tunable parameters to alter potency, others may simply be markers
Assayed value Duration of a step
Pot
ency
Pot
ency
Correlation=0.45 Correlation=0.38
35 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Derive insight from models to change
processes
Tradeoffs between model accuracy and timeliness
Vaccine Manufacturing Oil Drilling Treating Patients
36 © Copyright 2015 Pivotal. All rights reserved. 36 © Copyright 2013 Pivotal. All rights reserved.
Internet of Things in Healthcare Improving Patient Outcomes and Increasing Efficiency
37 © Copyright 2015 Pivotal. All rights reserved.
Beyond monitor alerts for crashing patients–Prediction means prevention Powering the Connected Hospital
ClinicalNarratives
38 © Copyright 2015 Pivotal. All rights reserved.
Use Cases in Healthcare Building a case for leveraging data and data science within a hospital setting
SAMPLE USE CASES � Prevent unnecessary ED visits using air quality and patient histories to anticipate needed
prescription refills � Avoid keeping patients longer than needed due to poor coordination by predicting patient length-of-
stay leading to on-time planning � Early alerts for deteriorating patients to increase monitoring and improve outcomes � Prevent discharging patients prematurely through patient readmission models � 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
39 © Copyright 2015 Pivotal. All rights reserved.
Data & Platform Overview
Pivotal HD
Pivotal HAWQ
DATA PLATFORM
TOOLS
� Data obtained from EPIC
� Total unique encounters: 242,312,567
� Total unique patient IDs: 11,195,934
� Encounters from 6 healthcare settings (including hospitals, skilled nursing facilities, ambulance and dialysis) – 8 total hospitals used in LOS – 2 regions
� 9 years of data
EPIC
DIAGNOSES PROCEDURES
LABORATORY VALUES
MONITOR FEEDS
BED OCCUPANCY
ORDERS
40 © Copyright 2015 Pivotal. All rights reserved.
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)
41 © Copyright 2015 Pivotal. All rights reserved.
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)
42 © Copyright 2015 Pivotal. All rights reserved.
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
# of
Adm
issi
ons
Hour of the day
# of
Dis
char
ges
43 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Derive insight from models to change
processes
Tradeoffs between model accuracy and timeliness
Vaccine Manufacturing Oil Drilling Oil Drilling
44 © Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil IoT in Oil & Gas
45 © Copyright 2015 Pivotal. All rights reserved.
Predictive Maintenance Drilling into the San Andreas Fault at Parkfield
California. Credit: Stephen H.
Hickman, USGS
� Failure costs estimated at $150,000/incident (billions annually)*
� Oil & gas generates large amounts of data from sensors enabling data-driven approaches to improve operations
� Goals – Early warning system – Insights into prominent features impacting operation and failure – Reduction of non-productive drill time – Reduced incidents
� But how do we build models?
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
46 © Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data? Predictive use cases Class of model Specific models
47 © Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
48 © Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
Predict remaining life of equipment Survival • Cox Proportional Hazards Regression
49 © Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
Predict remaining life of equipment Survival • Cox Proportional Hazards Regression
Predict rate-of-penetration in drilling Regression
• Linear Regression • Elastic Net Regularized Regression (Gaussian) • Random Forest
50 © Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
Predict remaining life of equipment Survival • Cox Proportional Hazards Regression
Predict rate-of-penetration in drilling Regression
• Linear Regression • Elastic Net Regularized Regression (Gaussian) • Random Forest
Identifying similar drilling sites Clustering • K-means
• Spectral clustering
51 © Copyright 2015 Pivotal. All rights reserved.
How are models built using BIG sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
Predict remaining life of equipment Survival • Cox Proportional Hazards Regression
Predict rate-of-penetration in drilling Regression
• Linear Regression • Elastic Net Regularized Regression (Gaussian) • Random Forest
Identifying similar drilling sites Clustering • K-means
• Spectral clustering
52 © Copyright 2015 Pivotal. All rights reserved.
How are models built using BIG sensor data? Predictive use cases Class of model Specific models
Predict equipment failure in time window Classification
• Logistic Regression • Support Vector Machines • Random Forest
Predict remaining life of equipment Survival • Cox Proportional Hazards Regression
Predict rate-of-penetration in drilling Regression
• Linear Regression • Elastic Net Regularized Regression (Gaussian) • Random Forest
Identifying similar drilling sites Clustering • K-means
• Spectral clustering
Oil & Gas industries may produce billions of data points across thousands of sensors. Most implementations of these algorithms do not scale
53 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Streaming Algorithm
� Finding linear dependencies between variables – ROP = c0+ WOB * cWOB
0 10 20 30 40 50 60 70 80 90
100 110
-10 15
Rat
e of
P
enet
ratio
n (R
OP
)
Weight on Bit (WOB)
54 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Streaming Algorithm
� Finding linear dependencies between variables
0
10 20 30 40 50 60 70 80 90
100 110
-10 15
Rat
e of
P
enet
ratio
n (R
OP
)
Weight on Bit (WOB)
55 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Streaming Algorithm
� Finding linear dependencies between variables
� How to compute with a single scan?
0 10 20 30 40 50 60 70 80 90
100 110
-10 15
Rat
e of
P
enet
ratio
n (R
OP
)
Weight on Bit (WOB)
56 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Parallel Computation
57 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Parallel Computation
Segment 1 Segment 2
58 © Copyright 2015 Pivotal. All rights reserved.
Linear Regression: Parallel Computation
Segment 1 Segment 2
59 © Copyright 2015 Pivotal. All rights reserved.
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
Exe
cutio
n tim
e (s
)
60 © Copyright 2015 Pivotal. All rights reserved.
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 • Logistic 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) • Support Vector Machines • 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
61 © Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models? Data Cleansing Libraries to Build
Models At-Scale Feature
Engineering
Derive insight from models to change
processes
Tradeoffs between model accuracy and timeliness
Vaccine Manufacturing Oil Drilling
62 © Copyright 2015 Pivotal. All rights reserved.
1 5 3 4 2 6 7
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63 © Copyright 2015 Pivotal. All rights reserved.
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64 © Copyright 2015 Pivotal. All rights reserved.
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SUNDAY THURSDAY TUESDAY WEDNESDAY MONDAY FRIDAY SATURDAY
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65 © Copyright 2015 Pivotal. All rights reserved.
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66 © Copyright 2015 Pivotal. All rights reserved.
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A Snapshot
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67 © Copyright 2015 Pivotal. All rights reserved.
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Another Snapshot
68 © Copyright 2015 Pivotal. All rights reserved.
CDC – 2011- Number of Health Care Visits Per Year - Age Adjusted
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Another Snapshot
69 © Copyright 2015 Pivotal. All rights reserved.
The Promise of Internet of Humans
� Smart contact lenses and sensors to identify and alert patients before catastrophic events (e.g. blood sugar drop for diabetics)
� Wearables to track patient disease progression using objective measures
� Track patient adherence � Detect disease outbreaks using sequencing in
sewer system samples � ECG monitoring on mobile phones for early
alerting of stroke
70 © Copyright 2015 Pivotal. All rights reserved.
http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!
71 © Copyright 2015 Pivotal. All rights reserved.
http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!
72 © Copyright 2015 Pivotal. All rights reserved.
http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!
73 © Copyright 2015 Pivotal. All rights reserved.
http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!
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http://blog.pivotal.io/data-science-pivotal
Check out the Pivotal Data Science Blog!
75 © Copyright 2015 Pivotal. All rights reserved.
FOR FURTHER INFO, CHECKOUT…
• Join us at our MeetUp tomorrow at 6:30 PM MADlib + HAWQ for advanced SQL machine learning on Hadoop Pivotal Labs 625 Avenue of the Americas, 2nd http://www.meetup.com/Pivotal-NY/events/225074025/
• Pivotal Blog @ http://blog.pivotal.io
• Pivotal Academy @ https://pivotal.biglms.com