=Paper=
{{Paper
|id=None
|storemode=property
|title=The Uncertainty Enabled Model Web (UncertWeb)
|pdfUrl=https://ceur-ws.org/Vol-679/paper10.pdf
|volume=Vol-679
|dblpUrl=https://dblp.org/rec/conf/enviroinfo/PebesmaCNS10
}}
==The Uncertainty Enabled Model Web (UncertWeb)==
https://ceur-ws.org/Vol-679/paper10.pdf
The uncertainty enabled model web
(UncertWeb)
Edzer Pebesma1 , Dan Cornford2 , Stefano Nativi3 , and Christoph Stasch1
1
Institute for geoinformatics, University of Münster, Germany,
Weseler Strasse 253, 48151 Münster, DE; edzer.pebesma@uni-muenster.de
2
Computer Science and NCRG, Aston University, Birmingham B4 7ET, UK
3
CNR and University of Firenze, Italy
Abstract. UncertWeb is a European research project running from
2010–2013 that will realize the uncertainty enabled model web. The as-
sumption is that data services, in order to be useful, need to provide
information about the accuracy or uncertainty of the data in a machine-
readable form. Models taking these data as imput should understand
this and propagate errors through model computations, and quantify
and communicate errors or uncertainties generated by the model ap-
proximations. The project will develop technology to realize this and
provide demonstration case studies.
Keywords: interoperability, uncertainty, OGC, workflows, geostatistics
1 Introduction
Environmental models are often complex. They need to represent space and time
in a discrete and referenced way, and typically need to cope with combinations
of points, lines, polygons, or regular grids, and potentially irregular points in
time (regular or irregular) or time intervals. For example, an air quality model
may accept sources (emissions) from areas (agricultural sources), lines (repre-
senting traffic along streets) and points (e.g. construction sites or factories). It
may discretize space as a regular grid in the horizontal direction and use vertical
layers of different thickness in order to solve the continuity equation that de-
scribes the change over time of the concentration of a component resulting from
transport, chemistry, dry and wet deposition, and emissions. As an input it may
already require wind velocity fields generated by a numerical weather prediction
model, or a Monte Carlo sample (ensemble) of these that summarize our limited
knowledge about the atmosphere.
According to [1],
The Model Web is a concept for a dynamic network of computer mod-
els that, together, can answer more questions than the individual models
operating alone [...]. It is based on a philosophy that encourages mod-
ellers to provide access to their models and model outputs via standard
“web services” [...], which makes it easier for the models to exchange
information.
Model inputs may be outputs from other models, but may also be sensor data,
e.g. temperature sensor readings. To function well, the model web should pro-
vide standard web interfaces to allow both access to model resources and data
resources. The seemingly sharp distinction between sensor readings and model
outputs might be not that sharp: sensors need to do some processing to translate
an electronic, analogue signal into a binary representation of a number reflecting
temperature in some unit, and models cannot distinguish between measured or
modelled values when they read them as input.
A major motivation to convert models and data sources into web services (or
encapsulate models in web service interfaces) that are standardized, is that it
allows an easier coupling of model and data resources, and allows the exchange
of resources to evaluate robustness to particular choices for model representa-
tions or data resources. When access to model and data resources is open, a
further motivation is that it democratizes access to scientific data and compu-
tation, and enhances the possibility to reproduce scientific research, and hence
the transperency of scientific activity.
In addition to openness about the procedures used to reach a given result,
a second pilar to obtaining transparency of scientific research is openness about
the limits of knowledge obtained. Measurements have limited accuracy. Models
are approximations to reality. Models using measurements result in values that
are not identical to the “reality” modelled. The most common way to represent
the degree of knowledge of a given quantity is by representing quantities through
probability theory4 .
The UncertWeb project (2010-2013) will realize instances of the model web,
and set it up in such a way that individual components of it (i) can under-
stand inputs that are encoded as uncertain quantities, for example described
using probability distributions rather than fixed values, (ii) can propagate er-
rors through the model computations, and (iii) can output errors generated as
a result of model approximation. At the time of writing this paper the project
has barely started, but a number of technological challenges that will be faced,
as well as the case studies that will be addressed, will be given.
2 Chaining environmental model web services
Web services need to advertise what they assume as inputs and outputs, in or-
der for machines to be able to check that they meet the assumptions made.
The industry standard for this is to us the SOAP/WSDL protocol. The Open
Geospatial Consortium (OGC, [2]), a standardisation body dedicated to geospa-
tial data handling, has made assumptions about OGC-compliant web services
that do not match the SOAP protocol. Using OGC Web Services (OWS) has
4
Probability theory is traditionally used to represent aleatory uncertainty (variability)
and epistemic uncertainty (lack of knowledge). An alternative that models concepts
as vague (sometimes called ontological uncertainty) rather than crisp in a quantita-
tive fashion is to use fuzzy set theory, although some argue that this can be addressed
within a subjective Bayesian perspective.
the advantage that software in the geospatial domain understands them (read:
clients exist that can work with their output), but has the disadvantage that
standard interactive, visual tools for web service chaining such as Kepler, Tav-
erna and BPEL enginges [4,3,6] for workflow orchestration do not work without
a lot of additional effort. Within UncertWeb we are exploring a number of ways
of integrating OWS with the SOAP/WSDL world.
2.1 O&M, WPS, profiling
Model webs, consisting of environmental models that are used for nowcasting
typically integrate current sensor data in the workflow. Sensor data can be pro-
vided over the OGC Sensor Observation Service, which yields either a SensorML
document or a document conforming to the Observations and Measurements
(short: O&M) conceptual model. Because observations and measurements can,
in principle, be anything from a temperature reading to a video stream to a
text message telling “It’s hot in here”, and time and space descriptions can have
many forms, this standard has to cope with a potentially huge number of possible
data representations. This makes it hard to predict whether an arbitrary O&M
compliant document will be accepted by a particular service. To limit the scope
of possibilities, so called profiles for particular applications or application do-
mains, can be developed as a workaround. This is important to allow developers
to support only the aspects of O&M that are required in the given application
context. Attemping to support the complete conceptual model would be beyond
the scope of any existing systems.
An OGC standard for a service that could execute a model is the Web Pro-
cessing Service (WPS). This service puts no constraints to the type of process
that can be executed, nor what the input should and output will look like.
Again, the advice is to develop a profile to describe the constraints on this in a
machine-readable way.
2.2 UncertML
Error propagagion, and in particular error propagation by Monte Carlo simula-
tion is not complicated in nature—instead of running a model once it is simply
run, say, 1000 times with inputs sampled from the distribution that reflects the
uncertainty of this particular input. The tricky bit is to organize the set of in-
puts and outputs when they have complicated structure (e.g. are a time series
of maps), and to derive relevant information from them.
One layer of information needed is that of semantics of the uncertainty: which
names do we give particular properties, such as the 10th realisation in a Monte
Carlo sample, the mean and variance, or the 95-percentile. UncertML [7] is the
starting point for a markup language to encode uncertain information. Uncer-
tWeb will improve and test this first version.In particular within UncertWeb,
UncertML will be separated into a simple conceptual model, a controlled vocab-
ulary and series of encodings.
2.3 Discovery
UncertWeb discovery functionalities will consider environmental models descrip-
tions expressed as specific metadata model profiles. These will include the extra
information needed to discover and chain models according to significant use
cases. The proposed solutions will consider the existing and ongoing specifica-
tions from relevant initiatives (e.g. GEOSS, GMES, INSPIRE, etc.). The profiles
will be based on well-accepted international standards, where appropriate.
As far as discovery and access are concerned, presently UncertWeb is inves-
tigating the following challenges:
a Uncertainty support for Scientific Data Types, with particular reference to
the next data model and encoding languages: Geography Mark-up Language
(GML), Obeservation&Measurement (O&M), and Common-Data-Model (CDM)
/ netCDF / ncML;
b An extended SOA approach addressing interoperability for uncertainty-enabled
sharing of environmental resources. A SOA brokering approach was success-
fully experimented in several GEO/GEOSS initiatives and FP7 projects (e.g.
GEO AIP, EuroGEOSS project, etc.) to reduce/solve interoperability issues
on discovery, access, and semantics functionalities.
c Support of uncertainty in discovery services; this concerns: (i) possible un-
certainty queryables, e.g. relative/average error on a data coverage; (ii) un-
certainty accommodation in standard metadata models, e.g. ISO 19115, IN-
SPIRE Metadata profile, etc.
2.4 Service chaining
As for chaining and publication methodology and services, presently UncertWeb
is defining the standard baseline for chaining, including standard services (e.g.
OGC services and ISO models) as well as communities of practice specification
and special interoperability arrangements, e.g. from the biodiversity community.
Besides, in keeping with the Model Web principles, UncertWeb is investigat-
ing a suitable approach for chaining uncertainty-enabled services, starting with
data and processing services. Different possible solutions (i.e. legacy frameworks
such as Kepler/Taverna/JOpera [4,3,5]; standard environments such as BPEL
[6]; Composition-as-a-Service, and Mash-ups that could include Google maps)
are compared evaluating their infrastructure complexity, entry level barrier, flex-
ibility, and portability.
An extended SoA approach, applied to the data service domain, promises
to facilitate workflows chaining and accomplish brokered data access for not
directly interoperable nodes. In fact, this approach could also enable the trans-
formation/mediation of uncertainty related metadata description and uncertain
scientific data-types.
2.5 Methods and tools for uncertainty management
Generic technology for building uncertainty enabled model webs is only par-
tially available, and UncertWeb will need to develop some more. Among these
are improved Monte Carlo methods, needed to reduce the number of model
runs required when model runs are computationally expensive. Other develop-
ments involve expert elicitation, a method where experts opinions are used, and
combined to obtain quantitative uncertainty measures for quantities for which
this is hard or impossible to obtain experimentally. Spatial, temporal or spatio-
temporal aggregation or disaggregation is often needed to obtain information
at a finer or coarser spatial resolution, and has consequences for uncertainty:
the average value for a country is easier to estimate than the value for each
point. The value, limitations and requirements to Monte Carlo simulation meth-
ods for error propagation will also be studied in the context of complex models
with high-dimensional parameter spaces. Finally, visualisation of uncertain in-
formation [10] is an active area of research, and is of particular importance to
communicate information to end users, who could be domain experts or members
of the public. Usability testing is foreseen to evaluate different alternatives.
3 Case studies
3.1 Biodiversity modelling
Fig. 1. Work flow for biodiversity modelling
The system developed in this case study (Fig 1) will enable land cover data
to be efficiently exchanged and validated, prior to being used to measure (mainly
anthropogenic) changes to protected zones. The model server that will be put in
place to compute and serve interoperable habitat information will benefit from
validated information, and the uncertainty framework developed in UncertWeb
will further allow us to quantify uncertainties in habitat changes. The coupling in
a Service Oriented Architecture (SOA) environment of this habitat model server
with climate change model servers will provide powerful new mechanisms to
policy makers and the scientific community for performing multi-scale analyses
of biodiversity-related problems and for forecasting ecological change.
3.2 Food chain modelling
Fig. 2. Work flow for food chain modelling
The prototype systems developed in biodiversity modelling work package
(Fig. 2) within UncertWeb will enable policy makers to integrate computer
models that simulate land-use and its change under different economic and cli-
matic scenarios. An important advance is the ability to propagate uncertainty
through the model Web: instead of focusing on highly constrained scenarios (as
in the FP6 LUMOCAP project, http://www.riks.nl/projects/LUMOCAP), we
can consider the impacts of uncertainty at the policy level. The model Web will
also allow us to consider other facets of the land-use problem, and we could
open up the possibility of using some more detailed environmental models (for
examples, see [11]).
3.3 Air quality modelling
The application of uncertainty-enabled forecasts for air quality (Fig. 3) is a logi-
cal and inevitable extension of numerical weather prediction ensemble forecasts.
Whilst current developments mainly focus on the regional scale, the concept
is just as applicable, and indeed more useful, for local authorities on the ur-
ban scale. It is expected that in the near future such ensemble forecasts will be
commonly available, and so it is essential to develop a communications system
suitable for dealing with such information.
3.4 Human activity modelling
Activity-based modelling of individual movements (Fig. 4) is an area that is just
entering the operational phase, because of its complexity, demands on input data,
and runtime requirements. The quantification of the prediction errors which
result from model approximation and input uncertainties is a novel application
Fig. 3. Work flow for air quality modelling
Fig. 4. Work flow for human activity modelling
area, but is required in order to acknowledge and manage the limited reliability
of outputs from realistic models.
3.5 Integrating air quality and human activity modelling
Successful integration of the two case studies on air quality forecasting and indi-
vidual activity modelling (Fig. 5) will be an important proof of concept for the
uncertainty-enabled model Web. The application of human activity modelling is
the first of its kind in Europe, but its predictions are subject to uncertainty. Us-
ing these uncertain predictions to feed an air quality model is a challenging and
innovative approach, and will allow assessment of the input (predicted emissions
from traffic) as well as assessment of exposure based on realistic allocation of
individuals in space and time.
4 Discussion
UncertWeb is an ambitious program that has the potential to demonstrate that
environmental model webs work, and can sensibly cope with uncertainty in a
Fig. 5. Work flow for integrating air quality and human activity modelling
quantitative way. A number of technological issues to realize this will have to be
tackled, solved or circumvented, in four very diverse case studies a number of
cutting edge challenges, including that of linking physical atmospheric modelling
(air quality) with social, agent-based human activity modelling while taking into
account the major sources of uncertainty. In particular we aim to address the
usability of the resulting system, providing tools to enable people to assess,
manage and visualise uncertainties in chains of web services.
Acknowledgements
This work has been funded by the European Commission, under the Seventh
Framework Programme, by Contract No. 248488 with the DG INFSO. The views
expressed herein are those of the authors and are not necessarily those of the
European Commission. More information on UncertWeb and UncertML can be
found on http://www.uncertweb.org/ and http://www.uncertml.org/, re-
spectively.
References
1. Geller, G.N. and Melton, F. Looking Forward: Applying an Ecological Model Web
to assess impacts of climate change. Biodiversity 9 (3&4), 79–83.
2. http://www.opengeospatial.org/
3. https://kepler-project.org/
4. http://www.taverna.org.uk/
5. http://www.jopera.org/
6. Tan W., Missier P., Madduri R., Foster I. (2009). Building scientific workflow with
Taverna and BPEL: A comparative study in caGRID. In: Proceedings. 4th In-
ternational workshop on Engineering Service-Oriented applications (WESOA), pp.
118129.
7. Williams, M., Cornford, D., Bastin, L. and Pebesma., E. (2009). Uncertainty
Markup Language (UncertML). Discussion Paper 08-122r1. Open Geospatial Con-
sortium. Available from http://portal.opengeospatial.org/files/?artifact_
id=33234.
8. OGC (2007a). Sensor Observation Service. Available from http://portal.
opengeospatial.org/files/?artifact_id=26667.
9. OGC (2007b). OpenGIS Web Processing Service. Available from http://portal.
opengeospatial.org/files/?artifact_id=24151.
10. E.J. Pebesma, K. de Jong, D.J. Briggs (2007). Visualising uncertain spatial and
spatio-temporal data under different scenarios: an air quality example. International
Journal of GIS, 21, 515527
11. EEA (2008). Modelling environmental change in Europe: towards a model inven-
tory (SEIS/Forward). EEA Technical report No 11/2008.