data quality and uncertainty visualization
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Data Quality and Uncertainty Visualization
UC San DiegoCOGS 220
Winter Quarter 2006Barry Demchak
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
Wiisard – Explosion with Pesticides
Wiisard – Network Deployment
Wiisard – Tablet Display
Wiisard – Command/Control
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 Tablet display shows out-of-date patient
information Summary displays are out of date, too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
DOD Example Sensor-to-shooter (STS) Networks – Patrick
Driscoll (USMA), June 2002
DOD Example
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.”
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
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
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)
Semiotic Interpretation
Data Visualization
Normal Mapping
Mapping
Normal
Data Visualization
Normal Mapping
PoorData
Quality
DataMapping
Data UncertaintyVisualization
Uncertainty Mapping
Mapping
Poor DataQuality w/
Uncertainty
Definition of Data Quality From Wand & Wang:
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
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??
GIS Examples – NCGIA
Sample point locations as overlay
Sample points and corresponding contours using naïve shading
GIS Examples – NCGIA
Gray shading uncertainty surface captures distance function used by interpolation method
Uncertainty encoded in contour line widths
Fill Clarity
Resolution
GIS Techniques
Contour Crispness
Fog
Merging Data and Uncertainty
Risk and uncertainty separately
Risk and uncertainty combined
Basic Data Examples Errors
Basic Data Examples Errors
Basic Data Examples Ambiguation
Basic Data Examples Ambiguation
Photo Realistic
Uncertainty Vector Glyphs
Uncertainty Vector Glyphs
Hue as Uncertainty With
out
With
Texture as Uncertainty
Raw
Trans-parent Points
Cer-tain-ty
Opaque Lines
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)(
Back to Wiisard
Individual data (annotation)
Aggregate data (annotated/integrated)
Back to Wiisard Annotated
Back to Wiisard Integrated
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
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
Conclusion
Data Quality and Uncertainty Visualization are like the weather …
… everyone’s talks about it, but no one does anything about it
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