Exchanging uncertainty: interoperable geostatistics?

Traditionally, geostatistical algorithms are contained within specialist GIS and spatial statistics software. Such packages are often expensive, with relatively complex user interfaces and steep learning curves, and cannot be easily integrated into more complex process chains. In contrast, Service Oriented Architectures (SOAs) promote interoperability and loose coupling within distributed systems, typically using XML (eXtensible Markup Language) and Web services. Web services provide a mechanism for a user to discover and consume a particular process, often as part of a larger process chain, with minimal knowledge of how it works. Wrapping current geostatistical algorithms with a Web service layer would thus increase their accessibility, but raises several complex issues. This paper discusses a solution to providing interoperable, automatic geostatistical processing through the use of Web services, developed in the INTAMAP project (INTeroperability and Automated MAPping). The project builds upon Open Geospatial Consortium standards for describing observations, typically used within sensor webs, and employs Geography Markup Language (GML) to describe the spatial aspect of the problem domain. Thus the interpolation service is extremely flexible, being able to support a range of observation types, and can cope with issues such as change of support and differing error characteristics of sensors (by utilising descriptions of the observation process provided by SensorML). XML is accepted as the de facto standard for describing Web services, due to its expressive capabilities which allow automatic discovery and consumption by ‘naive’ users. Any XML schema employed must therefore be capable of describing every aspect of a service and its processes. However, no schema currently exists that can define the complex uncertainties and modelling choices that are often present within geostatistical analysis. We show a solution to this problem, developing a family of XML schemata to enable the description of a full range of uncertainty types. These types will range from simple statistics, such as the kriging mean and variances, through to a range of probability distributions and non-parametric models, such as realisations from a conditional simulation. By employing these schemata within a Web Processing Service (WPS) we show a prototype moving towards a truly interoperable geostatistical software architecture.

UncertWeb processing service:making models easer to access on the web

Models are central tools for modern scientists and decision makers, and there are many existing frameworks to support their creation, execution and composition. Many frameworks are based on proprietary interfaces, and do not lend themselves to the integration of models from diverse disciplines. Web based systems, or systems based on web services, such as Taverna and Kepler, allow composition of models based on standard web service technologies. At the same time the Open Geospatial Consortium has been developing their own service stack, which includes the Web Processing Service, designed to facilitate the executing of geospatial processing - including complex environmental models. The current Open Geospatial Consortium service stack employs Extensible Markup Language as a default data exchange standard, and widely-used encodings such as JavaScript Object Notation can often only be used when incorporated with Extensible Markup Language. Similarly, no successful engagement of the Web Processing Service standard with the well-supported technologies of Simple Object Access Protocol and Web Services Description Language has been seen. In this paper we propose a pure Simple Object Access Protocol/Web Services Description Language processing service which addresses some of the issues with the Web Processing Service specication and brings us closer to achieving a degree of interoperability between geospatial models, and thus realising the vision of a useful 'model web'.

Researching XBRL as a socio-technical object

The Extensible Business Reporting Language (XBRL) is a grammar based on XML that is defined and described in the XBRL 2.1 specification. Instance documents are created by combining XBRL taxonomies and linkbases with data (facts) for a particular context. An alternative view is, XBRL is a mechanism for communicating information for decision-making between interested parties based on a generally accepted way of representing and digitally transmitting symbols of actions and events. XBRL may be both of these and many other things depending on how we frame our methodological understanding for the purposes of research. In this section we present an approach that conceives XBRL as a socio-technical object in the tradition of post-social perspectives (Knorr Cetina 1997; Latour 1996, 1999). © Deutscher Universitäts-Verlag GWV Fachverlage GmbH, Wiesbaden 2007.

XBRL:an (open) source of enlightenment or disillusion?

Extensible Business Reporting Language (XBRL) is being adopted by European regulators as a data standard for the exchange of business information. This paper examines the approach of XBRL International (XII) to the meta-data standard's development and diffusion. We theorise the development of XBRL using concepts drawn from a model of successful open source projects. Comparison of the open source model to XBRL enables us to identify a number of interesting similarities and differences. In common with open source projects, the benefits and progress of XBRL have been overstated and 'hyped' by enthusiastic participants. While XBRL is an open data standard in terms of access to the equivalent of its 'source code' we find that the governance structure of the XBRL consortium is significantly different to a model open source approach. The barrier to participation that is created by requiring paid membership and a focus on transacting business at physical conferences and meetings is identified as particularly critical. Decisions about the technical structure of XBRL, the regulator-led pattern of adoption and the organisation of XII are discussed. Finally areas for future research are identified.

Probabilistic uncertainty in an interoperable framework

This thesis provides an interoperable language for quantifying uncertainty using probability theory. A general introduction to interoperability and uncertainty is given, with particular emphasis on the geospatial domain. Existing interoperable standards used within the geospatial sciences are reviewed, including Geography Markup Language (GML), Observations and Measurements (O&M) and the Web Processing Service (WPS) specifications. The importance of uncertainty in geospatial data is identified and probability theory is examined as a mechanism for quantifying these uncertainties. The Uncertainty Markup Language (UncertML) is presented as a solution to the lack of an interoperable standard for quantifying uncertainty. UncertML is capable of describing uncertainty using statistics, probability distributions or a series of realisations. The capabilities of UncertML are demonstrated through a series of XML examples. This thesis then provides a series of example use cases where UncertML is integrated with existing standards in a variety of applications. The Sensor Observation Service - a service for querying and retrieving sensor-observed data - is extended to provide a standardised method for quantifying the inherent uncertainties in sensor observations. The INTAMAP project demonstrates how UncertML can be used to aid uncertainty propagation using a WPS by allowing UncertML as input and output data. The flexibility of UncertML is demonstrated with an extension to the GML geometry schemas to allow positional uncertainty to be quantified. Further applications and developments of UncertML are discussed.

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.

INTAMAP: an interoperable automated interpolation web service

INTAMAP is a web processing service for the automatic interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the open geospatial consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an open source solution. The system couples the 52-North web processing service, accepting data in the form of an observations and measurements (O&M) document with a computing back-end realized in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a new markup language to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropies and extreme values. In the light of the INTAMAP experience, we discuss the lessons learnt.

INTAMAP: the design and implementation of an interoperable automated interpolation web service

INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52°North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.

Describing and communicating uncertainty within the semantic web

The Semantic Web relies on carefully structured, well defined, data to allow machines to communicate and understand one another. In many domains (e.g. geospatial) the data being described contains some uncertainty, often due to incomplete knowledge; meaningful processing of this data requires these uncertainties to be carefully analysed and integrated into the process chain. Currently, within the SemanticWeb there is no standard mechanism for interoperable description and exchange of uncertain information, which renders the automated processing of such information implausible, particularly where error must be considered and captured as it propagates through a processing sequence. In particular we adopt a Bayesian perspective and focus on the case where the inputs / outputs are naturally treated as random variables. This paper discusses a solution to the problem in the form of the Uncertainty Markup Language (UncertML). UncertML is a conceptual model, realised as an XML schema, that allows uncertainty to be quantified in a variety of ways i.e. realisations, statistics and probability distributions. UncertML is based upon a soft-typed XML schema design that provides a generic framework from which any statistic or distribution may be created. Making extensive use of Geography Markup Language (GML) dictionaries, UncertML provides a collection of definitions for common uncertainty types. Containing both written descriptions and mathematical functions, encoded as MathML, the definitions within these dictionaries provide a robust mechanism for defining any statistic or distribution and can be easily extended. Universal Resource Identifiers (URIs) are used to introduce semantics to the soft-typed elements by linking to these dictionary definitions. The INTAMAP (INTeroperability and Automated MAPping) project provides a use case for UncertML. This paper demonstrates how observation errors can be quantified using UncertML and wrapped within an Observations & Measurements (O&M) Observation. The interpolation service uses the information within these observations to influence the prediction outcome. The output uncertainties may be encoded in a variety of UncertML types, e.g. a series of marginal Gaussian distributions, a set of statistics, such as the first three marginal moments, or a set of realisations from a Monte Carlo treatment. Quantifying and propagating uncertainty in this way allows such interpolation results to be consumed by other services. This could form part of a risk management chain or a decision support system, and ultimately paves the way for complex data processing chains in the Semantic Web.

Interactive data and retail investor decision-making:an experimental study

The results of an experimental study of retail investors' use of eXtensible Business Reporting Language tagged (interactive) data and PDF format for making investment decisions are reported. The main finding is that data format made no difference to participants' ability to locate and integrate information from statement footnotes to improve investment decisions. Interactive data were perceived by participants as quick and 'accurate', but it failed to facilitate the identification of the adjustment needed to make the ratios accurate for comparison. An important implication is that regulators and software designers should work to reduce user reliance on the comparability of ratios generated automatically using interactive data.