data quality and uncertainty visualization uc san diego cogs 220 winter quarter 2006 barry demchak
TRANSCRIPT
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Data Quality and Uncertainty Visualization
UC San DiegoCOGS 220
Winter Quarter 2006Barry Demchak
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Immediate Motivation: Wiisard
A joint project of Veterans Administration and UC San Diego, funded by the National Library of Medicine
Mass casualty triage and treatment Enter patient information via PDAs Patient information summarized on tablet PCs Command/control for supervisors and incident
comment personnel Tied together using 802.11b and store-and-
forward database access
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Wiisard – Explosion with Pesticides
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Wiisard – Network Deployment
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Wiisard – Tablet Display
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Wiisard – Command/Control
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Wiisard – The Problem
What if the network becomes partitioned? Tablet display shows out-of-date patient
information Summary displays are out of date, too
How does this lead to bad decisions? Supervisors may mis-deploy doctors Incident command may mis-deploy resources
People may die
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DOD Example
Sensor-to-shooter (STS) Networks – Patrick Driscoll (USMA), June 2002
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DOD Example
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DOD Example
“… our first attempt to get the military community to realize that there is a degree of uncertainty involved in (digital) information systems that cannot be engineered out of thesystem.”
“Ultimately, our concern was an awareness issue (for the decision maker) …”
“… woman at MITRE had proposed a system of tagging intelligence starting at the source in a way that would reflect the uncertainty of the data being put into the intel database.”
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The Problem
How to visualize the uncertainty in data so that humans can exercise judgment in making the best decision
Accounting for uncertainty is not the same thing as visualizing uncertainty
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What Labs are Involved
MIT Sloan School of Management Richard Wang (Data Quality)
Penn State University Alan MacEachren (GIS)
University of Maine Kate Beard-Tisdale (GIS)
University of California, Santa Cruz Alex Pang (Scientific Visualization)
University of Arkansas, Little Rock Master of Sciences in Information Quality
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What Conferences are There?
MIT Information Quality (IQatMIT) ACM SIGMOD Workshop on Information Qua
lity in Information Systems (IQIS) ACM SIGKDD (Knowledge Discovery and Dat
a Mining) MIT International Conference on Information
Quality (ICIQ)
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Semiotic Interpretation
Data Visualization
Normal Mapping
Mapping
Normal
Data Visualization
Normal Mapping
PoorData
Quality
DataMapping
DataUncertaintyVisualization
Uncertainty Mapping
Mapping
Poor DataQuality w/
Uncertainty
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Definition of Data Quality
From Wand & Wang:
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Metrics
Timeliness How up to date relative to intended purpose
Ballou et al: Timeliness = Max(0, 1-(currency/volatility) Currency = delivery_time – input_time Volatility = length of time data remains valid Apply sensitivity factor “s”: Timeliness ^ s
Tim
elin
ess
time
Tim
elin
ess
time
Pulse = 80 Pulse = 180
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Interplay with Uncertainty
Metrics are application dependent Metrics are data dependent Metrics are user dependent Question: If a metric describes an individual
data element, what is the effect of aggregating data elements having uncertainty??
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GIS Examples – NCGIA
Sample point locations as overlay
Sample points and corresponding contours using naïve shading
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GIS Examples – NCGIA
Gray shading uncertainty surface captures distance function used by interpolation method
Uncertainty encoded in contour line widths
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Fill Clarity
Resolution
GIS Techniques
Contour Crispness
Fog
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Merging Data and Uncertainty
Risk and uncertainty separately
Risk and uncertainty combined
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Basic Data Examples
Errors
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Basic Data Examples
Errors
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Basic Data Examples
Ambiguation
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Basic Data Examples
Ambiguation
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Photo Realistic
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Uncertainty Vector Glyphs
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Uncertainty Vector Glyphs
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Hue as Uncertainty
Without
With
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Texture as Uncertainty
Raw
Trans-parent Points
Cer-tain-ty
Opaque Lines
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Data Confidence
x is a device, is decay constant, R(x) is a weighting for device x in the calculation
Back to Wiisard
x
xpingtimexposttimecurtime
xRC
)(
)(1
1)(
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Back to Wiisard
Individual data (annotation)
Aggregate data (annotated/integrated)
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Back to Wiisard
Annotated
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Back to Wiisard
Integrated
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Research Questions
What are the dimensions of metrics relevant for determining data quality for medical providers in a mass casualty context?
What kind of visualization best conveys the use suitability for various kinds of data? Single data points Streaming bioinformation Aggregated information
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Research Questions
What kinds of visualizations are best suited to field personnel? Non-IS frenzied technicians High glare, small footprint screens Low processing power
What kinds of visualizations are best suited to incident command? Seasoned experts Large, high density displays Highly connected with high data processing
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Conclusion
Data Quality and Uncertainty Visualization are like the weather …
… everyone’s talks about it, but no one does anything about it