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Uncertainty and Quality UncertML, UncertWeb and Geoviqua. Dan Cornford, Matthew Williams Computer Science, Aston University, Birmingham, United Kingdom, OGC meeting, Bonn, Mar 2011. The key aspects of data quality. Think about why we worry about data quality …

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uncertainty and quality uncertml uncertweb and geoviqua

Uncertainty and Quality UncertML,UncertWeb and Geoviqua

Dan Cornford, Matthew Williams

Computer Science, Aston University, Birmingham, United Kingdom,

OGC meeting, Bonn, Mar 2011

the key aspects of data quality
The key aspects of data quality

Think about why we worry about data quality …

There is not universal agreement on what aspects of data quality are important but we might propose:

accuracy (uncertainty): value correctly represents the real world

completeness: degree of data coverage for a given region and time

consistency: are rules to which the data should conform met

usability: how easy is it to access and use the data

traceability: can one see how the results have arisen

utility: what is the user view of the data value to their use-case

Uncertainty is key – need to define the “real world”

accuracy and uncertainty
Accuracy and uncertainty
  • I view reality as a set of continuous space-time fields of discrete or continuous valued variables
    • the variables represent different properties of the system, e.g. temperature, land cover
    • note of course there are other feature based examples
  • A big challenge is that reality varies over almost all space and time scales:
    • we need to be precise about these when defining reality
  • Providing uncertainty (accuracy) allows:
    • combination of multiple data sources
    • propagation through further processing
    • decision making in a principled and understood framework
uncertainty in geospatial data
Uncertainty in geospatial data
  • We know very little about reality with certainty
  • Everything we know is derived from a sensor
    • all sensors measure with some uncertainty (location / support of measurement), value of measurement result (electronics), sensor model (many sources), ...
  • The result of a sensor measurement is a value (or a series of values) – where is the uncertainty?
    • the value is known, but is subject to various sources of errors, i.e. it is uncertain w.r.t. reality (resultQuality)
  • Other outputs, e.g. from models might be intrinsically uncertain - result
how to represent uncertainty
How to represent uncertainty?

Bayesian probability is the natural framework

this leads us to think about everything as a random quantity, which can be described by a probability distribution – maybe a Dirac delta if you are certain

sometimes we might not have a complete distribution and we might get: samples or summary statistics

Once we have a distribution we can use the tools of probability theory to do inference etc

knowing data has passed a QC is less useful – it has less information

Probability separates uncertainty from utility (QA4EO also seeks this split)

uncertainty in current systems
Uncertainty in current systems

Typically uncertainty about a (sensor) result is encoded in the resultQuality which is of type DQ_Element (xml:anyType)

This is fine, but has limited interoperability on an implementation level

Example using SWE Common

<QuantityRange definition="urn:ogc:def:property:OGC:tolerance2std">

<value>-0.02 0.02</value>

</QuantityRange>

<om:Observation gml:id="I90579489_12412">

<om:samplingTime>

<gml:TimeInstant xsi:type="gml:TimeInstantType">

<gml:timePosition>2009-12-19T13:41:10</gml:timePosition>

</gml:TimeInstant>

</om:samplingTime>

<om:procedure xlink:href="urn:ogc:object:feature:Sensor:WU:I90579489"/>

<om:resultQuality>

<!– any XML type -->

</om:resultQuality>

<om:observedProperty xlink:href="urn:ogc:def:phenomenon:OGC:temperature"/>

<om:featureOfInterest xlink:href=“http://www.mydomain.com/foi” />

<om:result uom="degC">0.6</om:result>

</om:Observation>

quality and uncertainty
Quality and uncertainty?

ISO19115 deals with metadata and (thus) quality

provides a framework in DQ_QuantitativeResult

the standard does not provide a dictionary for what errorStatistics can go in here – interoperable?

not clear that ISO19157 will address that

What to do if it is my result that is uncertain?

following processing this will almost always be the case

UncertML is a vocabulary + conceptual model and encoding for uncertain information

provides support for distributions, statistics and samples

aim to cover a very wide range of uses: SWE, SBML, Semantic Web, the Web

uncertml 2 0
UncertML 2.0

UncertML 2.0 refines UncertML 1.0:

removes dependencies making schema simpler and more usable

clearly identifies the conceptual model, the controlled vocabulary and the encodings (XML, JSON)

provides an API for using UncertML, being developed

Main reasons for further development are that uncertainty is a cross cutting aspect (remove dependencies), and to increase interoperability using hard typed design

Within UncertWeb, UncertML is being used in O&M, NetCDF and ISO19139 DQ_QuantitativeResult elements (presented in SWE WG)

Key aim is to provide simple, complete description of uncertain values

summary
Summary
  • UncertML 2.0 is designed to be easy to use
    • within UncertWeb it is the primary method for communicating uncertainty between components in workflows
    • within GeoViQua we are proposing UncertML as a means of improving the treatment of data quality in GEOSS, alongside QA4EO
  • Addressing uncertainty in a principled manner is critical to rational decision making and optimal use of available information

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreements n° [248488 and 265178].

further information
Further information

Further details from:

UncertML: www.uncertml.org

UncertWeb: www.uncertweb.org

GeoViQua: www.geoviqua.org

Dan Cornford (d.cornford@aston.ac.uk)

Matthew Williams (williamw@aston.ac.uk)

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreements n° [248488 and 265178].