Transcript Intelligent Systems: Big Data, Machine Learning, and Planning

Analytics for IoT:
From Sensors to
Decisions
Tom Dietterich
Distinguished Professor
Oregon State University
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The Usual View of IoT
Networks
Transducer
Array
Receive Beamformer Channels
420 µm
1000 µm
TS
∆Σ-M
Systems
TS
550 µm
TS
∆Σ-M
N
Channels
650 µm
TS
Σ
DSP
Processing
Processing
Elements
Receiver
TS
∆Σ-M
TGA
Transmitter
TS
Variable Length Delay Line
Devices
Materials
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Sensors
123RF Limited
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Spatio-Temporal Analytics
Hierarchy
Models
Events &
Activities
Trajectories
State
Variables
Cleaned
Data
Sensors
123RF Limited
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Leadership in Analytics
Machine Learning, Data Mining, Data Science
Hector Cotilla-Sanchez
Tom Dietterich
Alan Fern
Xiaoli Fern
Thinh Nguyen
Raviv Raich
Scott Sanner (joining April 1)
Prasad Tadepalli
Arash Termehchy
Sinisa Todorovic
Weng-Keen Wong
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Step 1: Data Cleaning and
Imputation
Broken
Sun
Shield
Cleaned
Data
o Detect bad data values and
broken sensors
o Interpolate good data values
as needed
Sensors
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Step 2: Estimate State Variables
o
o
o
o
State
Variables
Location of each customer
Location of each employee
Total time customer in store
# of items
Cleaned
Data
Sensors
123RF Limited
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Step 3: Record trajectories
Trajectories
o Trajectory of each customer
o Trajectory of each employee
o Trajectory of instrumented item
State
Variables
Cleaned
Data
Sensors
123RF Limited
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Step 4: Event and Activity
Recognition
Events &
Activities
o
o
o
o
o
o
o
Trajectories
State
Variables
Cleaned
Data
Customer enters/exits store
Employee enters/exits store
Customer waiting at help desk
Customer is looking for item
Customer picks up item
Customer puts item in basket
Customer leaves store with
unpaid merchandise
Sensors
123RF Limited
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Step 5: Predictive Models
Models
Events &
Activities
Trajectories
o Predict customer demand
per item
o Predict customer traffic
o Predict supplier delays
o Predict wholesale and retail
price trends
State
Variables
Cleaned
Data
Sensors
123RF Limited
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Bridging from Sensing to Action
Events &
Activities
Trajectories
State
Variables
Cleaned
Data
Alert
Sensor Needs
Repair
Sensors
123RF Limited
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Bridging from Sensing to Action
Events &
Activities
Trajectories
State
Variables
Enter Store
DB Update:
Increment Visit
Count
Cleaned
Data
Sensors
123RF Limited
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Bridging from Sensing to Action
Events &
Activities
Trajectories
Alert: Employee
to greet
customer
State
Variables
Alert: Cashier
needed
Cleaned
Data
Alert: Employee
to help
customer find
item
Alert: Possible
shoplifting
Coupon Offer to
cell phone
Sensors
123RF Limited
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Offline Analytics
Store Layout
 pinch points
 hot spots
 dead spots
Employee training
 conversion rate
 customer satisfaction
Inventory and staffing
 missed sales because
customer could not find item
 missed sales because out
of stock
 predictive inventory
management
 predictive staffing
 what predicts customer time
in store?
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Beyond Retail:
Personalized Medicine
Actions
Medical devices
Smart walkers; Exoskeletons
Models
Normal state variables, gait
Normal events and activities
Events &
Activities
Unsteadiness, falls, breathing difficulties
heart attack, stroke
Running, walking, climbing stairs
Trajectories
Trajectory travelled
Glucose history
State
Variables
Patient state (glucose, heart rate, EKG)
Current location
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Smart Buildings
Actions
HVAC automation
Adjust mix of energy sources
Time-shift predictable loads
Models
Building temperature in response
to external weather, HVAC controls
Events &
Activities
Holidays, Special events
Repair work, cold snap, heat wave
Energy price changes
Trajectories
State history
State
Variables
Room occupancy, CO2 level
Temperature, humidity, lighting
Exterior weather
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Analytics for a
Research-to-Market IoT Center
Data Quality Control
Computer Vision
Modeling
User Interface and User Experience
Security and Privacy
System Executive and Control
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Technology Needs: Data Cleaning
 Sensor diagnosis – Detect known sensor failure modes
 Anomaly detection – Detect novel sensor failures
 Imputation: learn and apply historical models to interpolate
missing or damaged data
 Data management
People:
o Tom Dietterich
o Alan Fern
o Weng-Keen Wong
o Xiaoli Fern
o Raviv Raich
o Arash Termehchy
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Technology Needs:
Computer Vision
 Tracking
 Pinch points
 Dead regions
 Hot spot detection
 Event & activity recognition
 walking, standing, bending over,
looking down/up
 picking up item, setting down
item, placing item in basket,
placing item in
bag/backpack/pocket
 waiting, browsing, searching for
something
 successfully finding item
 frustration, impatience
 shopping “mode” (“on a specific
mission”, “browsing”, “long list”)
 Face recognition
 Item recognition
People:
o Sinisa Todorovic
o Alan Fern
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Technology Needs:
Modeling
 Discovering Interesting Patterns in
Data
 Integration with External Data
Sources
 Web site visits
 Purchase History
 Third Party Customer Models, Social
Networks
 Shopping Apps
People:
o Weng-Keen Wong
o Xiaoli Fern
o Ron Metoyer
o Eugene Zhang
o Scott Sanner
 Optimizing Where and When RealTime Analytics are Computed
 Data Management
 Interactive Information Visualization
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Technology Needs:
User Interface/User Experience
 Employee cuing (handheld? ear piece? smart watch?)
 Customer interaction via smart phone and kiosk
 Long-term customer relationship
 Long-term employee relationship
People:
o Margaret Burnett
o Chris Scaffidi
o Martin Erwig
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Technology Needs:
Cybersecurity & Privacy
 Encryption
 Query-Specific Differential Privacy
 Software Quality and Testing
 Intrusion and Advanced Persistent Threat Detection
 Anomaly Detection
People:
o Rakesh Bobba
o Amir Nayyeri
o Attila Yavuz
o Mike Rosulek
o
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o
o
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Danny Dig
Alex Groce
Tom Dietterich
Alan Fern
Weng-Keen Wong
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Technology Needs:
Control Executive / Integration
All components of the system must coordinate
to ensure
excellent experience for the customer
excellent experience for employees
minimize costs (power, bandwidth, analysis)
Algorithms for control
Optimization
Planning
Reinforcement Learning
People:
o Ted Brekken
o Alan Fern
o Prasad Tadepalli
o Tom Dietterich
o Thinh Nguyen
o Yue Zhang
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Oregon State Advantages
Tightly-integrated School of Electrical Engineering
and Computer Science
Many EE⟺CS collaborations
Consistent Federal funding
Strong entrepreneurial culture
CASS: Center for Applied Systems and Software
Close ties to College of Business via the Division of
Engineering and Business
Easy routes to collaboration with industry
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