Download Synthesis of Streaming Data from Multiple Sensors via Embedded

Synthesis of Streaming Data from Multiple
Sensors via Embedded Data Extraction
April 15th, 2004 Project Report
Magdiel Galán
CSE591: DataMining
Dr. Huan Liu
Spring 2004
http://www.public.asu.edu/~mgalan/StreamProjApr15.ppt
Outline
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Problem/Project Description
Sampling
Smoothing
Clustering
Current Status
Plans
Project Description
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Synthesis of Streaming Data from Multiple
Sensors (~100’s) via Embedded Data
Extraction for mission critical applications.
Work in conjunction with Motorola’s Human
Interface Lab (on-going project)
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Simulation Environment
Project Description
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Goal: Develop driver assistance system that provide
feedback, but not control, during unsafe instances.
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From distractions caused by cellphones, PDAs, eMail,
Why: Targeting a government initiative to create a
safer car environment in the information age explosion
How: Develop intelligent system by mining Streaming
Data from multiple automotive sensors
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Development work being done using driving simulator with
projections screens with up to 400 parameters/sensors
including video links for eye-gaze and foot-pedal movement
Sample Cases
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Case Scenario #1:
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Passing Slow Traffic
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which slowed down due to an accident
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which you are also rubber-necking
 while fidgetting with your radio
Case Scenario #2:
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Making a left turn
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while hearing directions from MapTracker
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while checking at the time because you are late
 while reaching for the cellphone with on-coming call
Simulation Environment
150 Simulated View
Driving Experience
Gas
Gas
Batt
EngineTemp
Acceleration
Lateral Acc.
PDA
GearShift
Oil
Air Bag
GPS
Driver
Internet
CellPhone
A/C
CD
Sonar Proximity Sensor RPMs
Wheel Rotation
Brake Pressure
Motivation
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Primary Interest: Robotics
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Merging of Sensors/Sensor Fusion
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Problem: decide agent’s next best action vs. a goal
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optical
proximity (IR, sonar, radar)
location (GPS, visual maps)
movement (actuators, rotations)
system (battery, temperature, bump switches)
Not too dissimilar from an Automobile environment
Other Applications:
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Manufacturing Environment
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Increase Yields/Productivity/Reduce Defects using quality
control daily monitor data (100’s  Parameters  1K’s)
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Pentium Ex.: Oxide Thickness, Poly Width, Boron Implant
Density, Plasma Etch eV’s, Litho PM, Diffuser RPMs, etc…
Stream Data Properties
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Numerical/Continuous
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Categorical
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Speed
Steering/Heading
Acceleration (Forward/Lateral)
Distance (Lane Edge, Vehicle on Front)
Lane Position
Gear: P/R/D/OD/L1/L2
Headlights On/Off
Radio/CD ON
Incoming Call
Sampling Rate: 60Hz
Critical/Special Conditions
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Left/Right Turn
Passing/Changing Lanes
U-Turn
Reverse
Tailgating
Not On Road
Some Warning Signs
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Lane Drifting
Erratic Behavior
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droopy eyes
eyes not facing the road
foot/pedal movement do not correspond with
road conditions
Incoming Call while performing Critical
Maneuver
Goal
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Identify Instances outside normal patterns
as an indication of an Abnormal Situation
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Hence – Need to draw Driver’s Attention to
Impending Situation
Ultimate Goal:
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Develop bootsrapping mechanism that
combines driving situation classifiers (i.e.
LeftTurn/Passing) together with instance
selection methods in active learning
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Bootsrapping – selecting high utility data for retraining
Instance Selection Properties
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Instance representative
Instance selection  reduce rows
Ideal outcome instance selection
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choose a data subset achieves same result as
whole data with little or no performance P
deterioration
Should be model independent
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∆ P(Mi) ≐ ∆P(Mj)
[LM01]
Problem#1: Sampling
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Initial step towards instance selection:
select representative subset…
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Divide into collection of elements which must
cover the whole population without
overlapping [GHL01]
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These are called sampling units
Sampling Results
Sampling at 10mS
(x-axis: signal duration; y-axis: count)
Problem#2: Smoothing
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Reduce/Filter out noise and outliers.
Smoothing Techniques used:
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Bin Median/Rolling Average [LM01]/[D03]
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Median preferred over Mean since less sensitive to
outliers
Tresholding/Bin Boundaries [LM01]/[HK01]
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10% offset treshold
PreSmoothing - RAW Data
x-axis: driving time elapsed in minutes
y-axis: speed(km/h); steering(degrees), heading(degrees)
RAW Data Map/Course
Route Map – starting point at (0,0)
Smoothing Results - Median
x-axis: driving time elapsed in minutes
y-axis: speed(km/h); steering(degrees), heading(degrees)
Smoothing Results - Median
Smoothing Results - Threshold
Smoothing Results - Threshold
Dr. Liu’s Incremental Instance
Selection Algorithm
Given: Data streams with instances I
Output: indicative instances
For each data stream
Do the following incrementally
Create a profile P for I
Check new instance i against P
if i is an outlier of P
Return i
else
Update P with i
End do
Outliers
Problem#3: Clustering
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Why?
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Data is Unclassified
Previous results using Numerical Data on most
significant key parameters
Develop clusters exemplifying ALL attributes
Select instances that do not belong to a cluster
as triggering mechanism
Stream Clustering Challenges
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Large “Unclassified” Data Base
Fast On-Line Resolution within small window
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0.5 – to 2 or 3 seconds
One Pass Only restriction (need fast I/O)
Mix of Numerical and Categorical Data
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Traditional algorithms do not work well for categorical
attributes (remember P/R/D/OD/L1/L2, or CD On)
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Centroid approach cannot be used
Hard to reflect the properties of the neighborhood of the
points
Memory Constraints
Clustering Techniques vs.
Streaming Data
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SVM
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Good at handling multidimensional data
Not good – need classified data, lots of I/O,
data in memory
BIRCH
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Good at handling mulidimensional data, large
databases; single scan, linear I/O time
Not good – predominantly for “numerical” type
of attributes; order dependent
Clustering Techniques vs.
Streaming Data (2)
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CURE (Clustering Using REpresentative)[D03]
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Good at handling outliers; hierarchical
Not good – random sampling (won’t fit
streaming)
ROCK (RObust Clustering Using LinKs)[D03]
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Good at Hierarchical clustering for categorical
attributes
Not good: Random sampling for scale up
My 1st Clustering Attempt…
Move in
Reverse
My 1st Clustering Attempt(2)
Zoom Next
Page
My 1st Clustering Attempt(3)
Move in
Reverse
Current Status/Plans
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This is an ON-GOING project
Cluster Technique Development
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Evolve from known methods?
Generalization of the technique
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Not just Automobile Streaming Data
References
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[LM01] H.Liu, H. Motoda. “Data Reduction via Instance Selection”. Instance
Selection and Construction for Data Mining. 2001. KAP. ASU Library
[GHL01] B. Gu, F.Hu, H. Liu. “Sampling: Knowing Whole From its Part”. Instance
Selection and Construction for Data Mining. 2001. KAP. ASU Library
[HK01] J. Han, M. Kamber. Data Mining Concepts and Techniques. Chps. 3, 8
Data Cleaning, Clustering. Morgan Kaufman. ASU Library
[D03] M.Dunham. Introductory and Advanced Topics. Prentice Hall, Chps. 3-5.
Mining Techniques, Classification, Clustering. ASU Library