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Open Source Tools for Uncertainty Enabling the Model Web

Open Source Tools for Uncertainty Enabling the Model Web. Benjamin Proß University of Münster FOSS4G, Denver, 12-16 Sept 2011. Overview. UncertWeb Data Encodings/Profiles Web Services Other Tools. UncertWeb Facts. Uncertainty-enabled Model Web, FP7 Project (http://www.uncertweb.org/)

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Open Source Tools for Uncertainty Enabling 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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