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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
Hadoop Summit, San Jose, CA
June 10th
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.
Connected devices take action
to make daily life easier.
But what else?
5© Copyright 2015 Pivotal. All rights reserved.
How can IoT help prevent
accidents like the Macondo
Disaster ?
6© 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
7© Copyright 2015 Pivotal. All rights reserved.
Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake
8© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…
9© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…
…become this?
10© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…
…become this?
11© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
 How is data collected?
 Where is it stored and processed?
 Is there real signal or just noise?
 How can we build a predictive model?
 When is the right time to take action?
12© Copyright 2015 Pivotal. All rights reserved.
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
13© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
How to build a
predictive model at
scale
Tradeoffs between
model accuracy
and timeliness
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling Vaccine Manufacturing
14© Copyright 2015 Pivotal. All rights reserved.
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 Vaccine Manufacturing
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness
Treating Patients
15© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
Data-driven paradigms, data cleansing and feature engineering
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
16© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
 Oil & gas exploration and production activities generate large
amounts of data from sensors
 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
17© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
 Oil & gas exploration and production activities generate large
amounts of data from sensors
 What opportunities exist for data-driven approaches to improve
operations?
Drilling operations Predictive maintenance
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
18© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
 Oil & gas exploration and production activities generate large
amounts of data from sensors
 What opportunities exist for data-driven approaches to improve
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
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
Predictive maintenance
19© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
 Oil & gas exploration and production activities generate large
amounts of data from sensors
 What opportunities exist for data-driven approaches to improve
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
$150,000/incident (billions annually)*
• Goals
– Early warning system
– Insights into prominent features impacting
operation and failure
– Reduction of non-productive drill time
– Reduced incidents
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
20© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling
21© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling
Integrated Data
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
22© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
23© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
Drill bit changes
24© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
25© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
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)
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World
IoT: How Data Science Driven Software is Eating the Connected World

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IoT: How Data Science Driven Software is Eating the Connected World

  • 1. 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 Hadoop Summit, San Jose, CA June 10th
  • 2. 2© Copyright 2015 Pivotal. All rights reserved. Our everyday devices are smart and talk to us
  • 3. 3© Copyright 2015 Pivotal. All rights reserved. These devices are now talking to each other
  • 4. 4© Copyright 2015 Pivotal. All rights reserved. Connected devices take action to make daily life easier. But what else?
  • 5. 5© Copyright 2015 Pivotal. All rights reserved. How can IoT help prevent accidents like the Macondo Disaster ?
  • 6. 6© 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
  • 7. 7© Copyright 2015 Pivotal. All rights reserved. Smart Systems = Sensors + Digital Brain + Actuators Problem Formulation Modeling Step Data Step Application Step Data Science for Building Models Sensors & Actuators Data Lake
  • 8. 8© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this…
  • 9. 9© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this… …become this?
  • 10. 10© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this… …become this?
  • 11. 11© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action?  How is data collected?  Where is it stored and processed?  Is there real signal or just noise?  How can we build a predictive model?  When is the right time to take action?
  • 12. 12© Copyright 2015 Pivotal. All rights reserved. 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
  • 13. 13© Copyright 2015 Pivotal. All rights reserved. Critical considerations for successful modeling How to build a predictive model at scale Tradeoffs between model accuracy and timeliness Data-driven paradigms, data cleansing and feature engineering Use Cases Oil Drilling Vaccine Manufacturing
  • 14. 14© Copyright 2015 Pivotal. All rights reserved. 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 Vaccine Manufacturing Derive insight from models to change processes Tradeoffs between model accuracy and timeliness Treating Patients
  • 15. 15© Copyright 2015 Pivotal. All rights reserved. Critical considerations for successful modeling Data-driven paradigms, data cleansing and feature engineering 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
  • 16. 16© Copyright 2015 Pivotal. All rights reserved. Data: The New Oil Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS  Oil & gas exploration and production activities generate large amounts of data from sensors  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
  • 17. 17© Copyright 2015 Pivotal. All rights reserved. Data: The New Oil Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS  Oil & gas exploration and production activities generate large amounts of data from sensors  What opportunities exist for data-driven approaches to improve operations? Drilling operations Predictive maintenance *http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
  • 18. 18© Copyright 2015 Pivotal. All rights reserved. Data: The New Oil Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS  Oil & gas exploration and production activities generate large amounts of data from sensors  What opportunities exist for data-driven approaches to improve 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 *http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry Predictive maintenance
  • 19. 19© Copyright 2015 Pivotal. All rights reserved. Data: The New Oil Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS  Oil & gas exploration and production activities generate large amounts of data from sensors  What opportunities exist for data-driven approaches to improve 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 $150,000/incident (billions annually)* • Goals – Early warning system – Insights into prominent features impacting operation and failure – Reduction of non-productive drill time – Reduced incidents *http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
  • 20. 20© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? Integrating & Cleansing Feature Building Modeling
  • 21. 21© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? Integrating & Cleansing Feature Building Modeling Integrated Data Primary data sources Operator Data ( thousands of records ) • Failure details • Component details • Drill Bit details Drill Rig Sensor Data ( billions of records ) • Rate of Penetration (ROP) • RPM • Weight on Bit (WOB)
  • 22. 22© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  • 23. 23© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  • 24. 24© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  • 25. 25© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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● ● ● ● ● 00:00 10:00 20:00 30:00 40:00 50:00 00:00 101520 df$ts_utc df$wob 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)

Editor's Notes

  1. 2010 accident on BP offshore platform in the Gulf of Mexico Drilling rigs cost between $350,000 and $1,000,000 per day Non-productive Time (NPT) is the measurement most watched Worst case scenario is a Macondo type blow-out = $40B liability We need - better safety protocols……better regulation……and a smart system!
  2. Aggregation-side We want a 360 view of a patient in order to understand what is happening to them. To get a better view of a condition. One of the biggest challenges is with the different types of data that are available. They range from things that we traditional view as structured data in that they have controlled terminologies to unstructured data that is often processed in some way to become structured. E.g. an image can be featurized in a way that extracts data.
  3. http://www.cdc.gov/nchs/data/hus/2011/083.pdf