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Open Source Tools for Uncertainty in the Model Web

This presentation highlights the UncertWeb project, a collaborative initiative designed to enhance the accountability of uncertainty propagation in web service chains. It focuses on the development of mechanisms, standards, tools, and encodings such as UncertML, enabling robust handling of uncertainty in various model applications, including climate change and air quality forecasts. With contributions from multiple partners, it aims to standardize uncertainty representation, ensuring informed decision-making through improved data quality and accessibility.

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Open Source Tools for Uncertainty in the Model Web

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  1. Open Source Tools for Uncertainty Enabling the Model Web Benjamin Proß University of Münster FOSS4G, Denver, 12-16 Sept 2011

  2. Overview • UncertWeb • Data Encodings/Profiles • Web Services • Other Tools

  3. UncertWeb Facts • Uncertainty-enabled Model Web, FP7 Project (http://www.uncertweb.org/) • Feb 2010 – Jan 2013 • 8 Partners • Aston University • Italian National Research Council • Food and Environment Research Agency • Joint Research Centre • Norwegian Institute for Air Research • Eindhoven University of Technology • University of Muenster • Wageningen University

  4. The UncertWeb concept • The “model web”. • When chaining services of limited or unknown quality, uncertainty must be accounted for if rational decisions are to be made. “UncertWeb develops mechanisms, standards, tools and test-beds for accountable uncertainty propagation in web service chains.”

  5. Definitions • Uncertainty • Model inputs • E.g. through measurement errors of sensors • Taking outputs of other models as inputs • Models itself • By aproximating reality, models introduce errors, and should inform the user about (annotate its output with) the degree this happens

  6. Application Scenarios • Biodiversity and climate change • Land-use response to climatic and economic change • Short term uncertainty-enabled forecasts for local air quality • Individual activity in the environment

  7. Data encodings

  8. Uncertainty Model Language (UncertML) • UncertML is a dictionary and encoding for uncertain information. • JSON and XML encoding • Provides support for distributions, statistics and realisations. • Aim to cover a very wide range of uses: SWE, SBML, Semantic Web

  9. Profiles

  10. Why Profiling? • Profiles: • specification or standard consisting of a set of references to one or more base standards and/or other profiles, and the identification of any chosen conformance test classes, conforming subsets, options and parameters of those base standards, or profiles necessary to accomplish a particular function • In other words: • Restricts a base standard to suite a certain application purpose

  11. UncertWeb Profiles • Vector Data: • GML Profile: Restriction of geometry types • ISO 19139 extension for uncertainty • O&M Profile: Spatio-temporal and result restrictions • Raster Data: • NetCDF-U: extension of NetCDF for uncertainty values

  12. UW Profiles – Vector Data • Goal: provide easy-to-use and simplified profiles • GML Profile used to encode vector-based geometries and features: • Restrictions on Geometries • Restrictions on Metadata • O&M Profile: • Uses GML Profile • Restrictions on result types • Common ways to encode uncertainties

  13. GML Profile • Spatial Geometries • Point • LineString • Polygon • Grid • Multigeometries • Collection for each of the geometries defined above (e.g. Multipoint, MultiLineString, etc.) • Temporal Geometries: • TimeInstant, TimePeriod • Uncertainty Property Type

  14. ISO 19139 Extension • Extends DQ_QuantitativeResult with DQ_UncertaintyResult • Used for resultQuality in O&M

  15. O&M Profile - restrictions • Spatial restrictions • to SamplingFeature as defined in O&M • Geometries of SamplingFeatures as defined in GML Profile • Temporal Restrictions • TimeInstant and TimePeriod according to GML profile • resultQuality has to be ISO 19139 data quality • Includes extension with Uncertainty result

  16. O&M Profile – Observation Subtypes • Measurement: • result is double with uom info • BooleanObservation • DiscreteNumericObservation • Result is integer • TextObservation • Result is String • UncertaintyObservation • Result is UncertML AbstractUncertainty • ReferenceObservation • Result is reference to e.g. file on a server

  17. O&M Profile - Collections • Collection for each observation subtype • MeasurementCollection • BooleanObservationCollection • …

  18. NetCDF-U • Network Common Data Format • support the creation, access, and sharing of array-oriented scientific data • Structure: Header + Body: • Header defines variables • Body contains binary-encoded variable values

  19. NetCDF-U Header Example URLs to UncertML Dictionary

  20. UncertWeb Java APIs • Goal: provide easy-to-use lightweight Java API for Information Models • Is used in Web Service implementations for encoding/decoding of inputs and outputs

  21. Web Services

  22. Web Services and Models • Models not developed with automation in mind. • Can require a user interface. • Wrappers enable them to be called with code (e.g. Java) in the service interface. • Write input files, run, parse output files into usable objects. • Mechanisms for running each model may vary substantially.

  23. Service interface overview • Open Geospatial Consortium (OGC): • WPS for processing (i.e. models). • Including utility services for translating between uncertainty types. • WCS, WFS, WMS, SOS for data access. • W3C Web Services (WS): • SOAP for information exchange. • WSDL for service description.

  24. Use of WPS • UncertWeb Proxy Service (UPS) • Proxy for (not uncertainty-enabled) model-WPS • Executes Monte Carlo simulations • Uncertainty Transformation Service (UTS) • Transforms Uncertainties • E.g. distributions to realisations • Spatio Temporal Aggregation Service (STAS) • Transforms different data into a common scale

  25. Uncertainty Transformation Service • Transforms Uncertainties • E.g. distributions to realisations • Can handle: • UncertML (UncertML parser/encoder) • NetCDF-U (UncertWeb binary parser/encoder) • O&M • R-backend

  26. UncertWeb Proxy Service • MonteCarloSimulation process • Inputs: • Identifier of the process to be executed by the model-WPS • Uncertain inputs for the process • Static inputs for the process • URL of the model-WPS • Output uncertainty type (e.g. distribution or realisations) • Number of realisations (i.e. monte carlo runs) • Outputs: • The specified uncertainty type

  27. UPS Samples Realisations Monte Carlo Service (UPS) LOOP 1 Sample 1 Realisation Model Service (WPS) 1 Sample 1 Realisation Model Implementation

  28. UPS + UTS PDF PDF Monte Carlo Service (UPS) PDF PDF Uncertainty Transformation (UTS) Uncertainty Transformation (UTS) Realisations Samples LOOP 1 Sample 1 Realisation Model Service (WPS) 1 Sample 1 Realisation Model Implementation

  29. Local Air Quality Model Chain UncertML JSON UncertML realisations O&M • Demo chain with UncertWeb components INTAMAP service (WPS) Air quality observations (SOS) Interpolation of background concentration UncertML + GML Uncertainty enabled Austal model (UPS + WPS + UTS) Overlay Service (WPS + UTS) Web-based Visualisation client Estimation of air pollution from local emissions at point locations Adding both outputs to final concentration map

  30. USOS/UWCS • Can handle: • UncertWeb O&M profile (USOS) • NetCDF-U (UWCS)

  31. SOAP/WSDL • SOAP protocol. • Use in combination with information models (GML, O&M). • Standard fault messages. • WSDL service description. • Available operations. • Required structure of messages, both request and response. • Where to find the service.

  32. SOAP/WSDL • Advantages: • Widely adopted by the rest of the web. • Tool support for generating code. • Compatibility with workflow software (Taverna, Kepler) and orchestration engines. • Disadvantages: • Lack of semantics. • Not fully adopted by the OGC.

  33. JSON • A lightweight format for exchanging data. • Preferred over XML for JavaScript client development. • Used by Google, Facebook, Twitter...

  34. Applying the technology • Web service supporting two interfaces: SOAP/WSDL and JSON. • Web service interface sits separately from model. • Wrappers need to be developed so the interface can communicate with the model.

  35. Other tools

  36. CaaS • Composition as a Service • Offers functionality to • Create uncertainty enabled model chains • Execute them

  37. Visualization tool • Different visualizations of uncertainties in spatio-temporal data

  38. Elicitator • Expert elicitation of uncertainties • E.g. parameters of distribution functions • Mean and standard deviation of a Gaussian normal distibution

  39. Sensitivity Analysis tool • Performs sensitivity analysis • Identify the influence of the different inputs to the model output

  40. Links • http://www.uncertweb.org/ • http://uncertml.org/ • https://svn.52north.org/svn/geostatistics/main/uncertweb

  41. Thank you Questions? The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement n° [248488].

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