=Paper=
{{Paper
|id=None
|storemode=property
|title=Visualizing Uncertainty in Environmental Workflows and Sensor Streams
|pdfUrl=https://ceur-ws.org/Vol-712/paper3.pdf
|volume=Vol-712
}}
==Visualizing Uncertainty in Environmental Workflows and Sensor Streams==
https://ceur-ws.org/Vol-712/paper3.pdf
Visualizing Uncertainty In Environmental
Work-flows And Sensor Streams
Karthikeyan Bollu Ganesh and Patrick Maué
Institute for Geoinformatics (IFGI), University of Muenster,
D-48151 Muenster, Germany
{karthikeyan,pajoma}@uni-muenster.de
Abstract. Environmental data and models are uncertain by nature.
The lack of knowledge about, for example, the magnitude of potential
measurement errors may lead to unforeseen consequences. This makes it
difficult to assess the data’s or model’s usefulness for critical applications.
We present an approach for the visualization of uncertainty coming from
in-situ environmental sensors. The visualization component is part of a
Web-enabled environmental modelling platform which also supports the
specification of processing workflows. The concept of uncertainty and
means for its encoding as part of the environmental data are introduced.
The individual components in the processing workflows propagate and
update the uncertainty information. We also explain how the uncertainty
in the original sensor data has been identified, and how the visualization
component has been implemented.
Keywords: Uncertainty,Uncertainty Visualization, Environmental Work-
flows
1 Introduction
The on-going implementation of the European INSPIRE directive facilitated the
access to geographic information on the Web. Embedded in Spatial Data Infras-
tructures (SDI), geospatial Web services provide means to access, process, and
visualize spatial data. Interoperability between different Web services is to a cer-
tain degree ensured by the standards set by the Open Geospatial Consortium.
The standards also enable the direct integration into local geospatial applica-
tions. But there has been only limited uptake by the environmental modelling
community. The concept of a Shared Environmental Information System (SEIS)
has been recently introduced to address this issue [2]. The published SEIS prin-
ciples aim to facilitate access to environmental information for public authorities
and the general public. This calls for new ways of how to publish environmental
services, and how to present the resulting information to also non-ICT skilled
end-users. Investigating the requirements for environmental services infrastruc-
tures, including the execution of environmental services via standard runtime
engines for service work-flows and the Web-enabled visualization through en-
2 Ganesh, Maué
vironmental portals, is subject of the research project ENVISION1 [6]. ENVI-
SION aims to provide an environmental services infrastructure with ontologies
which investigates the distributed execution, semantic discovery and annotation
of environmental services. Results from our research regarding the encoding,
propagation, and visualization of uncertainty are presented in this paper.
2 Environmental Models As Workflows
Environmental information is traditionally resulting from environmental com-
puter models. Simple computer models could perform atomic computations,
such as interpolating the elevation within the DEM (Digital Elevation Model).
To solve complex problems such as predicting the impact of an oil spill on the
local wildlife, the chaining of individual simple models might be required. In a
first step, a weather forecasting service encapsulated as an OGC Web Processing
Service takes real-time weather data from an OGC Sensor Observation Service
to predict future weather conditions. The result is taken as input for the oil
drift model, which computes the dispersion and weathering of an oil slick for
the forecasting period. As final step, this distribution may be used to assess the
impact of the oil slick on the local wildlife (e.g. using ESI2 maps). Each of these
models are implemented as WPS, the coupling with other Spatial Data Services
(SDS) serving the input is done with the Business Process Modelling Language
(BPEL). Standard BPEL engines can then execute these workflows.
3 Uncertainty In Environmental Data
Results from the workflows help domain experts in their decision-making tasks,
e.g. for assessing response measures for an oil spill. Oil spills could be fought
by distributing detergents, skimming, or even burning. Which method to apply
depends on the results of the oil spill model. Taking the wrong decisions here
could potentially have devastating effects on the environment; having correct
and highly certain results is therefore crucial. Much of the data that forms the
input for the computer models contains some sort of uncertainty. Environmental
information is uncertain by nature. ”You are uncertain, to varying degrees, about
everything in the future; much of the past is hidden from you; and there is a
lot of the present about which you do not have full information. Uncertainty is
everywhere and you cannot escape from it” [5].
3.1 Uncertainty in Work Flows
Uncertainty is an expression of confidence about our knowledge [4]. Uncertainty
results from imprecise measurements of environmental phenomena. It is pro-
cessed using geospatial algorithms, which generalise, infer, or merge data to
1
More information available at http://www.envision-project.eu
2
ESI stands for ”Environmental Sensitivity Index”
Visualizing Uncertainty 3
generate new data. Uncertainty is part - and therefore an important aspect -
of GI throughout the complete services work flow, starting with the creation of
the data until its visualisation on a map [3]. The following two types of uncer-
tainty should be explicitly quantified to enable users to assess the quality of the
end product of environmental service chains. Input errors result from imprecise
measurements, either due to human error or insufficient sensing technology. The
human factor or wrongly calibrated sensors are hard to quantify, while errors
coming from sensors not sensitive enough can be known beforehand. Model er-
rors emerge during the processing of the data, e.g. the interpolation of unknown
values from sparse input data. The uncertainty of the resulting product depends
on both, input and model error. That requires a solution to add uncertainty
information to the data, and to keep and update uncertainty information while
processing the underlying data.
Processing uncertain environmental data propagates the uncertainty often
unpredictably [8]. Environmental models may introduce their own model errors,
which influence the original uncertainty of the input observational data. The
introduced error depends on the selected algorithms. This work is focussing on
the visualization of uncertainty coming from work flows of environmental data
and processing services. Discussing how to compute the uncertainty coming from
the models is out of scope of this paper. This is addressed by the research cur-
rently performed in the UncertWeb3 project; here we have to assume that the
processing components in the work flow are able to propagate and update the
uncertainty parameters. Finding adequate visualization techniques for uncer-
tainty is an active research topic. Examples for such techniques are portraying
uncertainty in graphs by showing normal distribution and confidence intervals,
colour models, or time-series charts. The following implementation presents a
tool showing the uncertainty in environmental data as charts.
4 Implementation
The implementation of this project is divided into the following sub-tasks: (1)
Identification of existence data quality parameters, (2) extending the standard
data format for sensor data with uncertainty information, and (3) visualizing
uncertainty in a graph.
4.1 Identification Of Existing Uncertainty Parameters
Information about data quality can be either qualitative (e.g. adding informa-
tion about the data provider to address issues such as trust) or, in most cases,
quantitative (e.g. completeness, accuracy, scale, and more). Data quality has
been acknowledged to play an important role for geographic information, and
OGC and ISO published standards for representing data quality parameters.
This work is focussing on the accuracy of sensor measurements. A piezometric
3
More information available at: http://www.uncertweb.org/
4 Ganesh, Maué
sensor system is used for ENVISION to monitor underground water levels. The
sensed data can be accessed through an OGC SOS4 interface. The accuracy
of the measured data from these piezometers is found to be around 1% of the
measured value5 .
4.2 Uncertainty Extensions For O&M 2.0
Sensed data always comes with some sort of error. From a conceptual perspec-
tive all data should be considered to be uncertain. Even though most data lacks
information about uncertainty, some data sets may have descriptive informa-
tion about it in its metadata (following the ISO 19013 standard for data quality
metadata). This global definition of uncertainty is many cases insufficient for
the visualization and assessment of the data set’s usefulness for critical appli-
cation. The Uncertainty Markup Language (UncertML) has been introduced as
extension to the OGC Geography Markup Language to address this issue. It
is focussed on an XML encoding for the transport and storage of uncertainty
information [8]. UncertML includes means to express simple summary statistics
(e.g., mean and variance) as well as complex representations such as parametric,
multivariate distributions at each point of a regular grid [7]. Uncertainty can be
encoded either in form of (1) statistics, e.g. values for probability or the quan-
tile, (2) distributions, e.g. a normal distribution, or as (3) realisations. UncertML
2.0 relies on the OGC Observation & Measurement (O&M) standard to encode
uncertainty [1]. The following listing for the piezometer observations encodes
Uncertainty as a Gaussian distribution. The mean value is to be understood
to be the actually sensed value, while the variance reflects the precision of the
sensor measurement.
0.89 0.87
0.01 0.01
Listing 1 - Example of uncertainty-enabled O&M 2.0
4
http://swe.brgm.fr/pleiade-core-service-ades-om2-0.0.1-recette/REST/sos?
Request parameters and examples are available at: http://sosades.brgm.fr/
5
As reported by the service providers BRGM, the French geological survey
Visualizing Uncertainty 5
4.3 Visualization
The implementation adopts the visualization through the charts. The component
expects the element Gaussian Distribution and computes the according uncer-
tainty intervals. A time-series chart displays the maximum, minimum value of
the uncertainty and the mean value of the incoming observations. The following
screen-shots includes the chart viewer on the left side, and the map showing the
according sensor positions on the right side. They belong to a set of components
developed in the ENVISION project. The individual modules are implemented
as Portlets (compliant to the Java Portlet Specification 2866 ), which can be best
described as pluggable user interface components for the Web. By simply se-
lecting one of the sensors displayed in the map, the according time series can
be visualized in the chart. The red line represents the actually observed values
(the mean), while the blue lines represent the according boundaries of the un-
certainty intervals. The chart is based on a JavaScript library7 which supports
rich interaction with the graph. The user can hover over the chart to see the
individual values at different points in time.
Fig. 1. Uncertainty Viewer in the Envision Infrastructure
5 Conclusion
The focus of this work is on the visualization of uncertainty resulting from mea-
surement errors and the processing of the environmental data. It relies on the
encoding of the uncertainty using the UncertML standard. How to come up with
6
More information available at: http://www.jcp.org/en/jsr/detail?id=286
7
More information available at: http://www.highcharts.com/
6 Ganesh, Maué
this uncertainty information - and how to propagate uncertainty in geospatial
work flows - has not been subject of this research. This is investigated in the re-
search project UncertWeb. Hence, future work will focus on integrating the other
features supported by UncertML in the visualization components. This includes
research on the usability, i.e. how can we best communicate uncertainty to sup-
port the end-users in the decision making process. In ENVISION the execution
of the geospatial work-flow is handled by a distributed execution infrastructure.
It includes techniques for the optimization of the work-flows, and the ad-hoc
adaptation of execution paths according to certain context parameters. Future
work will also investigate how uncertainty information may contribute to this
adaptation process.
6 Acknowledgements
This work has been funded by the European research project ENVISION (FP7-
249170, see http://www.envision-project.eu)
References
1. Cox S.: Observations and Measurements. Technical report 05-087, OGC. (2006)
2. European Commission. COM 46 final: Towards a Shared Environmental Information
System (SEIS). Communication from the Commission. (2008)
3. Grønmo, R., Roman, D., Llaves, A., & Maué, P.: ENVISION D3.1: MaaS
Composition Portal - Architecture specification. Available at http://www.
envision-project.eu (2010)
4. Heuvelink, G. B. M., Brown, J. D., & Loon, E. E. van.: A probabilistic framework
for representing and simulating uncertain environmental variables. International
Journal of Geographical Information Science, 21(5), 497-513. (2007)
5. Lindley, D. V.: Understanding Uncertainty (p. 272). Wiley-Interscience (2006)
6. Roman D., S. Schade, A. Berre, N. Bodsberg, and J. Langlois.: Environmental Ser-
vices Infrastructure with Ontologies - A Decision Support Framework. In Proceed-
ings of EnviroInfo Conference 2009, Berlin, Germany. (2009)
7. Williams, M., Cornford, D., Bastin, L., & Ingram, B.: UncertML : an XML schema
for exchanging uncertainty . Proceedings of GISRUK, Manchester, UK. (2008)
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122r: Uncertainty Markup Language (UncertML). Available at: http://portal.
opengeospatial.org/files/?artifact_id=33234 (2009)