P Dihé

S Frysinger

R Güttler

S Schlobinski

L Petronzio

R Denzer

S Nesic

T Lobo

G Schimak

J Hrebicek

M Donatelli

An Architecture for the Semantic Enhancement of Environmental Resources

Keywords: SISE, semantics, search, discovery, annotation, tagging, resources

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Search and discovery have been staples of the modern Internet culture since the release of the first practical browser. But these were followed in short order by the need to save discovered information through "bookmarking." This allowed an individual user to keep track of web-based resources that were interesting to them, and it added value to the information in that the facts of its prior discovery and selection constituted some additional information: acceptance. That is, the individual had already searched for, discovered, and somehow approved of the resource, and creating a bookmark captured this additional information.

But is that the limit of the possibilities of web-based information enrichment? Could a user attach more detailed semantic content to a discovered resource to help them use it in the future? Could the attachment of such information be independent of the particular computer on which they were working? Could they share such semantic enhancements with others? Would such capabilities facilitate the use of highly distributed web-based resources -such as environmental data and services -in the solution of complex problems being addressed by many individuals and organizations distributed across many organizations and nations?

These questions are critical to the notion of a Single Information Space in Europe for the Environment (SISE), an important effort on the part of the European Commission to harmonize and facilitate environmental decision-making. The vision of SISE requires seamless access to environmental resources, including data, models and services. But these resources themselves also have the potential for semantic content that is related to the viewer of the resource.

Consider a simple example. A restaurant aficionado might find a reference to a particular Italian restaurant and, having been pleased by the chef's generous use of garlic, might annotate this restaurant favourably. This will be useful when this particular customer is choosing a restaurant for a future dinner. But it might also be useful for another user who, sensitive to garlic, would choose to avoid this restaurant because of this very same characteristic.

One could imagine a very similar situation occurring within the context of environmental data and services. A particular data set, for example, may be valuable for some applications because of a characteristic that, for other applications, renders it inappropriate.

Environmental data and models are generally very dependent on the conditions of their capture or generation, respectively. Rarely would an astute environmental manager base a decision on a data set without understanding the conditions under which the data were obtained, in order to ensure that those conditions were consonant with the decision problem at hand. Similarly, the use of modelling results must be conditioned on the consistency between the assumptions made in the model with those in the question being decided.

In order to achieve the objectives of SISE, it will be necessary to find a way to capture and report "auxiliary semantics" associated with data and services discoverable on the web. These auxiliary semantics would, in general, be independent of the original author of the data or service, and would be understood to be subjective, at least to the extent that they represent the interpretation of a viewer of the resource, as opposed to the generator of the resource.

Requirements of SISE

As described by Hřebíček and Pillmann [START_REF] Hřebíček | Shared Environmental Information System and Single Information Space in Europe for the Environment: Antipodes or Associates?, European conference of the Czech Presidency of the Council of the EU TOWARDS eENVIRONMENT[END_REF] and the Inspire Forum [START_REF]Inspire Forum, Single Information Space in Europe for the Environment (SISE)[END_REF], the concept of the Single Information Space in Europe for the Environment (SISE) was introduced in the European Commission's Framework Programme 7 (FP7) in association with objective ICT-2007.6.3: ICT for Environmental Management and Energy Efficiency. The central idea is to support real-time connectivity to and seamless search of multiple environmental resources, across borders and disciplines, with facilitated data acquisition and sharing, along with service chaining on the web. Several workshops were conducted.

As reported by O'Flaherty [START_REF] O'flaherty | Towards a Single Information Space for the Environment in Europe[END_REF], following an Expert Consultation Workshop in Brussels in February of 2008, "[t]he central requirement of the SISE will be to enable technical interoperability, driven by two key trends:

1. Complexity Management: requiring a holistic approach, multi-disciplinary research, and an infrastructure accessible to all disciplines, communities and actors. 2. Environmental Legislation in Europe: driving higher requirements for common environmental monitoring and reporting."

Six dimensions of SISE were identified at this workshop: The assembled experts agreed about the following prioritisation of topics for future research and innovation actions on the SISE:

These dimensions and priorities lead naturally to some high-level requirements of SISE, which may be briefly summarised as follows:

• Support the discovery of relevant resources • Facilitate integration of and seamless access to data sources residing on a standard based infrastructure (such as those provided by SANY or ORCHESTRA) • Support repositories (e.g. databases, caches, inventories) for quality controlled and securely managed resources and their results • Incorporate integrated security in order to control access to resources (e.g. data sources, catalogues, whether one's own or third party resources) • Preserve the possibility to publish results on the web for public access • Provide easy to use tools and user-friendly services and interfaces, e.g. access control, workflow management, delivery management, visualisation, data extraction, and administration, embedded in the users' semantic context.

The deployment of a Single Information Space for the Environment in Europe is inhibited by the lack of a mechanism allowing data and service users to provide annotations that would add value to these resources. A middleware infrastructure is needed to fill this gap between environmental resources and end users. This framework needs to facilitate the life-cycle utility of environmental information from its collection and persistent storage to its discovery and purpose-oriented exploitation. Such a goal can be achieved through coherent, transparent, efficient and context dependent (e.g. semantically enhanced) discovery mechanisms, pre-processing services that translate meaning and structure of information, accompanied by services or information items (e.g. data) tailored to end-user needs, and explanatory facilities.

TaToo's open approach to this need allows it to contribute not only to a single European Information Space for Europe, but also to the requirements of sharing information as expressed in the requirements of the SEIS (Shared Environmental Information System).

Needs, Gaps & Objectives of TaToo

Given the objectives and requirements of SISE, as described in Section 2, the TaToo project targets one major objective:

To contribute to the elimination of obstacles in discovery as well as the contextsensitive interpretation and use of environmental resources within SISE by developing easy to use tools within a semantic framework for discovery, access, and interpretation in a multilingual and multi-domain context.

This objective was approached first by conducting two needs & gaps analyses to identify the most pressing issues regarding the enhancement of the annotation and discovery process of environmental resources (i.e. data and services). Users were asked which types of environmental resources are important to them (e.g. geospatial data, structured raw data, time series, etc.); for which purposes the user needs the discovered resources (e.g. input data for models); how the user currently searches for relevant resources (e.g. search engines, data catalogues, etc.); and how discovered resources can currently be accessed (downloads, web services, etc.).

Users are mostly interested in various types of data ranging from time series data to data aggregated in reports or documents, as well as software and mathematical models. Common to most types of data requested is that they should be related to a specific geographical location. Users particularly want additional meta-information that helps them to assess whether a resource is usable for their clearly defined purpose. This includes, for example,

• information about the quality of the data, • how data were collected, and by whom,

• whether and which interpolation methods were applied, and • the applicability of models and algorithms to a certain domain.

Thus, one of the crucial features of TaToo is the ability to provide appropriate and rich semantic meta-information about a resource during the discovery process.

Regarding the discovery of environmental resources, most users rely on classical search engines but would prefer a solution that combines the usability and simplicity of search engines with the power and advanced features of semantic discovery. The most common problems that need to be addressed are related to low precision of the results, because the search results are often cluttered with too many irrelevant or unsuitable resources. Another issue is that resources exposed through services (in particular OGC services) often cannot be found by classical search engines. Users would also like to be able to perform discovery that takes their context (e.g. user profiles, user relationships, user location, search history, etc.) into account in order to improve results or to discover new relevant resources. Furthermore, they want to be able to search for links between related topics, e.g. for data that are available for the same spatio-temporal scope and pertain to two different thematic domains (e.g. distribution of persistent organic pollutants and cancer incidence statistics).

Regarding the different means to tag resources, or to view, alter and evaluate tags attached with resources, users raised the concern that multiple tags for the same meaning could emerge. They recommended that TaToo take standardised thesauri and dictionaries into account and provide a mechanism to propose new tags that can be added to existing ontologies.

Most users would use a portal as the main system entry point, but integration of TaToo functionality with their current systems is also a topic of great interest. Therefore, they appreciated the possibility of accessing the TaToo framework through public service interfaces in order to create their own custom clients.

Concerning the question of whether and how the users would be willing to use and contribute to TaToo, the vast majority indicated that they would like to use TaToo to search for and interpret environmental resources. Users also requested tools to enable them to tag resources in a manner specifically related to their field of expertise in order to improve the discovery of environmental resources in general. Furthermore, the ability to easily register their own resources and to harvest an initial set of metainformation during a (semi-) automatic registration process is a highly desired feature.

4

The TaToo Architecture

TaToo's primary aim is to provide the functionality to discover and tag resources with metadata in order to improve future discovery through the information enrichment process described above. In order to offer this kind of functionality, the TaToo framework has to include a set of system components (on the server side) providing the implementation of the functionality, and a set of user components (on the client side) helping the end users interact with the core components through graphical user interfaces (GUI) [START_REF] Schimak | Information enrichment using TaToo's semantic framework[END_REF].

In the context of the TaToo objectives stated previously, the TaToo architecture must incorporate a number of properties and principles, such as the re-use of existing software wherever possible, in order to avoid wasted development effort, and the use of data-and interface-related standards from the relevant communities, where appropriate, to assure seamless integration into existing infrastructures. Moreover, TaToo must provide easy to use GUI clients for all TaToo functions to a wide range of users, and provide well-defined interfaces (e.g. APIs and standardised web service interfaces) allowing third parties to embed TaToo functionality in their own applications.

The TaToo Framework Architecture has to consider the facilities for data and information discovery, visualisation, and evaluation/validation, as well as tagging/annotation by humans and the harvesting of meta-information about resources by (semi-) automated processes.

A coherent architectural design approach, especially in the context of SISE and the TaToo objectives stated in Section 2, demands careful consideration of crucial functional requirements as well as the adoption of well-proven architectural design procedures which result in service oriented architectures that meet essential architectural properties like openness, interoperability, extensibility, flexibility and loose coupling. For this reason, the conceptual foundations of the TaToo architecture follow an evolutionary approach based on experience from former infrastructure projects and sound procedures already established in ORCHESTRA [START_REF] Usländer | Reference Model for the ORCHESTRA Architecture v.2.1, OGC "Best Practices[END_REF] and SANY [START_REF] Usländer | Specification of the Sensor Service Architecture v3.2[END_REF]. Among the predominant procedures which are applied in the architectural design and specification of the TaToo framework are the separation of concerns, the logical grouping of functionalities, the specification of open and standards based interfaces, the introduction of an implementation independent layer, and the establishment of a domain-independent generic infrastructure. This ultimately leads to a multi-tier architecture composed of interacting components, which is not influenced by implementation specific restrictions, technological choices, or a specific thematic domain.

When talking about conceptual foundations of the architecture, one has to also consider supporting elements like guidelines, templates and tools that help architects and developers to specify and develop their components and domain ontologies in a structured, methodical and consistent manner. In this spirit, the TaToo architecture doesn't only specify the TaToo framework, but it also provides both the methodology and the tools to effectively facilitate the initial architectural specification as well as the further extensibility of the TaToo framework, including the support for arbitrary thematic domains. Extensibility is a key feature for TaToo, since it allows third parties to register their own resources for semantic annotation, to plug in their own domain ontologies and to contribute to the TaToo framework itself with their own specialised client applications and semantic tagging and discovery services. Thus, the TaToo community will greatly benefit from the framework's open nature.

The TaToo architecture consists of an implementation independent Functional Purview and an Implementation Purview (Figure 1). Implementation independence at the architecture level leads to a more sustainable architecture that is not vulnerable to technology changes in the future, and it is thus able to accommodate changes in technology without changing the architecture itself. This is especially important because TaToo intends to make use of existing solutions, such as widely adopted semantic frameworks like Sesame and Jena.

Fig. 1.TaToo Framework Architecture

The Functional Purview therefore introduces an implementation independent layer that is mapped to a concrete implementation platform, resulting in a reference implementation of the TaToo framework. At first, the Functional Purview specifies the conceptual foundations of the architecture, including the definition of the architectural design process followed by its objectives and structural elements (Tiers and Building Blocks). It also explains the considerations and decisions that resulted in the chosen architectural definition process, which follows widely accepted principles of software architecture. Additionally, it establishes the conceptual principles for discovery and tagging in TaToo as well as an ontology framework that allows the production of formal, semantically enhanced resource descriptions. Finally, it provides the actual implementation independent specification of the TaToo framework consisting of the descriptions and functional specifications of all TaToo components, and an illustration of their basic interactions with the help of a set of core use-cases.

The Implementation Purview complements the Functional Purview in the areas of implementation and technology. The focus of the Implementation Purview lies on how a certain component is realised in detail, the formal specification of the component's interfaces and information exchanged, and the specification of a Minimum Environmental Resource Model (MERM).

The elements of architectural design that define the overall structure of the TaToo Framework are Tiers and Building Blocks. Tiers logically separate general concerns, like presentation and business logic, while Building Blocks and the components defined therein share a common set of properties and organise and group more concrete functionalities.

The specification process in service-oriented architectures often stops at the service interface level. The TaToo architecture goes one step further by specifying the functionalities of client components and underlying business logic components in addition to the publicly visible functionality of the system exposed through the interfaces and operations of services. Therefore, Figure 2, which shows only Building Blocks, illustrates some essential components and the most important information flows. The TaToo framework architecture is designed as an n-tier architecture that currently consists of the following 4 tiers:

1. The Presentation Tier, which is concerned with user interaction and the presentation and aggregation of information. 2. The Service Tier decoupling the Business Tier and Presentation Tier, as well as serving as a layer to enforce interoperability through the provision of well defined and self-describing service interfaces. 3. The Business Tier, which is responsible for the core functionality (business logic) of the TaToo System. 4. The Data Tier, which is concerned with the storage of semantically enriched information, and other data (registered resources to be harvested, user's information, etc.).

Fig. 2. TaToo Framework Architecture Tiers and Building Blocks

Besides the tiers, the second elements of architectural design are the Building Blocks (Figure 3). The architecture is composed of two main high level Building Blocks: the User Components Building Block and the System Components Building Block. The User Components Building Block is composed of User Components (also called TaToo Tools), such as tagging and discovery tools, with a graphical user interface that supports the interaction with a human user.

Fig. 3. TaToo Framework Architecture Building Blocks

The System Components Building Block is further decomposed into the Public Services Building Block and the Core Components Building Block. The Public Services Building Block is composed of Public Services, like tagging and discovery services, which expose TaToo functionality to User Components through well defined (specified) interfaces. The Core Components Building Block provides the business logic, data and meta-information management, thus insulating complex semantic procedures from the rest of the system. These components are only accessed by other System Components and by the TaToo Public Services. Consequently, interoperability is less of a concern and the System Components may, but need not, be accessible over standardised web service interface.

TaToo Architecture -Added Value for SISE

TaToo is very ambitious in its goal of providing added value to the domain of discovery and annotation of environmental resources. TaToo provides the appropriate means to create easy to use discovery mechanisms and tools, and facilitates discovery with each iterative tagging cycle. Discovery becomes more precise and information quality and value are increased.

TaToo mitigates the burden of creating 'meaningful' -i.e. semantically enhancedmeta-information by preparing the ground for information enrichment of the discovered resources. One of TaToo's benefits is that when handling a resource with a known semantic context TaToo supports users in their effort to add annotations and ranking information (e.g. according to importance, quality, uncertainty, etc.).

More concretely, TaToo delivers tools facilitating discovery of environmental resources through the use of underlying semantic structures. It provides open specifications of semantic discovery frameworks and tools and substantially contributes to the fulfilment of the requirements of SISE. TaToo provides a public Service tier to deal with arbitrary resources and in particular with resources that are based on open standards. TaToo's ability to deal with a variety of ontologies in a multilingual context, depending on the current discovery strategy, strives to overcome well-known problems in this area.

Due to the iterative improvement and enhancement of descriptive information through TaToo's tagging cycles, supported by an intelligent tagging process, decision support quality increases significantly.

Going back to the SISE imperatives identified in Section 2, we can explicitly state the manner in which TaToo supports fulfilment of each of these imperatives.

Regarding the six dimensions of SISE:

1. User Dimension: Actor centric and adaptable to the expectations and needs of many types of users.

The TaToo architecture deals with this dimension on technical as well as functional levels. On the technical level the TaToo architecture foresees user components including a portal that can be tailored to different user types, and specific user-centric client applications that can use the public service interface of the public services (such as tagging and discovery). On the functional level the TaToo architecture establishes no restrictions on the type of resources for which TaToo functionality is offered, thus supporting the needs of many types of users.

Content Dimension:

A global information space including models, knowledge, services and tools.

The TaToo architecture is open to easy integration of new services, tools, knowledge, or any kind of resources.

3. Data Dimension: Fusing multiple heterogeneous sources, sensors, platforms, networks and visualisations.

Any kind of resource coming from any kind of source can be the object of TaToo functionality (tagging, discovery, evaluation, etc.).

Modelling & Decision Support Dimension: Involving distributed virtual teams and communities

TaToo supports decision-making by offering the decision maker valuable metainformation of different kinds (e.g. "rating information", evaluation information about resources of different types) introduced by other users, thus supporting community building.

Information and Service Dimension:

Stable and secure multilingual services, using semantically enhanced, ad hoc on-demand service chaining on the web.

The TaToo architecture anticipates a set of public services designed according to standards or best practises with standardized security measures. Semantic metainformation supports the use of these services.

6. Other SISE Dimensions: Including standardisation, ethics/privacy, data policy, IPR and liability

The TaToo architecture calls for the use of standard approaches wherever possible.

Regarding the technical requirements of SISE:

• Support the discovery of relevant resources

The TaToo architecture offers a public discovery service making use of different kind of semantic meta-information.

• Facilitate integration of and seamless access to data sources residing on a standard based infrastructure (such as those provided by SANY or ORCHESTRA)

TaToo offers meta-information about resources like data sources, including information about access procedures and semantic meta-information about suitability of the use of a data source in a given situation.

• Support repositories (e.g. databases, caches, inventories) for quality controlled and securely managed resources and their results

TaToo doesn't directly support repositories, but it supports the discoverability and usability of repositories by offering corresponding meta-information.

• Incorporate integrated security in order to control access to resources (e.g. data sources, catalogues, whether one's own or third party resources)

The TaToo architecture provides security measures for access by the public to meta-information about resources and TaToo services, but TaToo does not directly support security concerning the resources themselves. Nevertheless, meta-information about a resource offered by TaToo can include meta-information concerning access control for a resource.

• Preserve the possibility to publish results on the web for public access The TaToo architecture supports the discoverability of published results as well as meta-information about them (e.g. concerning their usability for a given purpose).

• Provide easy to use tools and user-friendly services and interfaces, e.g. access control, workflow management, delivery management, visualisation, data extraction, and administration, embedded in the users' semantic context.

The TaToo architecture foresees different kinds of user components offering userfriendly access to TaToo functionality, such as a portal, or public services to be accessed by user-centric client applications.

Generalization and future research

In this paper we have introduced an architecture for the semantic enhancement of web-based environmental resources that serves to enhance the discoverability and usability of these resources with semantic technologies in the context of the vision of a Single Information Space in Europe for the Environment (SISE). The architecture is built upon widely accepted architectural design principles and addresses in its first iteration the core functionalities, tools and concepts necessary to leverage semantic discovery and annotation of resources in a multi-domain context.

Future work on this architecture will take into account further research and innovation on SISE and will be engaged in activities to

• continuously enhance existing services and tools and identify new services and tools according to user needs • develop an integrated user context management and access control concept incorporating the Friend of a Friend FOAF vocabulary and the OpenID standard for decentralized authentication. • update TaToo's environmental resource model and the adjacent ontology framework in order to leverage domain ontologies development and provide support for cross-domain mappings • invent new discovery strategies to improve context-aware and domain-oriented discovery • develop a common model that allows inference with ontologies in different languages, in order to support multilingual tagging and discovery • adopt the so-called linked data approach for the semantic web [START_REF] Bizer | Linked Data -The Story So Far[END_REF] in order to enable TaToo to join the ever-growing linked data community, and thus to access and discover semantically annotated resources on a global scale

Acknowledgments

TaToo is a Collaborative Project (contract number 247893) co-funded by the Information Society and Media DG of the European Commission within the RTD activities of the Thematic Priority Information Society Technologies.


Ari Jolma
email: ari.jolma@aalto.fi

Kostas Karatzas

Software Architectures for Distributed Environmental Modeling

Keywords: Environmental modeling, Distributed information system, Software architecture, Workflow

Environmental modeling is increasingly more integrative and collaborative work. The Internet and Web hold a promise of a shared environmental information infrastructure, which supports modeling. While the traditional (enterprise) information system architectures have not been much employed in the environmental domain, new standards-based Web technologies create an opportunity. At the same time the modeling workflows are being investigated, and this work may provide a fruitful starting point for new kind of top-down services that support modeling within environmental information infrastructures.

Introduction

Developing purposeful and credible environmental models that consider all relevant aspects of the problem and integrate heterogeneous information is an acknowledged goal [START_REF] Jakeman | Ten iterative steps in development and evaluation of environmental models[END_REF], [START_REF] Denzer | Generic integration of environmental decision support systems -state-of-theart[END_REF]. Environmental modeling is collaborative work involving teams of experts, stakeholders and model users. Interconnecting data and tools in information systems is a traditional method for solving information processing problems. During the last 15 years information systems technology has been embracing the Internet and the Web [START_REF] Alonso | Web Services: Concepts, Architectures and Applications[END_REF], changing the paradigm of information management to information exchange [START_REF] Aberer | Emergent Semantics, Invited talk at the ICSWN 2004 conference in Paris[END_REF]. Internet is a natural platform for information infrastructures that link information systems. "Shared Environmental Information System" is a concept and initiative put forward by the European Commission and European Environment Agency [START_REF]Shared environmental information system[END_REF]. Information infrastructure that supports environmental modeling allows interconnecting environmental models with one another and with data. Collaboration that is based on setting up and using information services promises to improve and help modeling.

Attaining the perceived goals of a shared environmental information infrastructure naturally assumes that useful data and services exist on the Web. This is indeed already the case to some degree but often the data and services are still oriented to humans and not programs. To use such services directly in applications is possible but error-prone and often includes cumbersome reverse-engineering. Web services are applications, which allow direct interactions with other applications using standardsbased protocols and interfaces [START_REF] Alonso | Web Services: Concepts, Architectures and Applications[END_REF]. Many datasets and real-time data are available or becoming such as web services [START_REF] Doyle | Introduction to OGC Web Services[END_REF], [START_REF] Horsburgh | An integrated system for publishing environmental observations data[END_REF]. Using models in the Web by human users and over the Web by other applications is in production use (our observation) and attempts to more standards-based approaches have been reported since early 2000's [START_REF] Kokkonen | Interfacing environmental simulation models and databases using XML[END_REF], [START_REF] Eder | Towards interoperable atmospheric (air flow) models in Spatial Data Infrastructures using OGC Web Services -state of the art and research questions[END_REF], [START_REF] Theisselmann | Service-oriented Architecture for Environmental Modelling -The Case of a Distributed Dike Breach Information System[END_REF], [START_REF] Granell | Service-oriented applications for environmental models: Reusable geospatial services[END_REF].

Modeling is much more than just preparing and connecting datasets to various models and running the models. It is very difficult to define modeling and its boundaries to development of model-based information systems and to model-based integrated assessment. For the purposes of this paper we consider modeling to be a process where data is used and processed and various software tools are used and developed to solve problems, answer questions, and provide support for planning and management. Important elements of modeling include objectives, conceptualization, assumptions, validation, consultation, and transparency [START_REF] Jakeman | Ten iterative steps in development and evaluation of environmental models[END_REF]. A shared information infrastructure for modeling needs to include tools for working and spelling out these.

In this paper we review some key concepts of distributed information system architectures in the light of environmental modeling and cases. Based on the review we discuss possible ways forward in developing shared information infrastructures for environmental modeling.

Fundamental Architectural Patterns

Distributed information systems traditionally have a three layer architecture comprising a resource layer, an application logic layer, and a presentation layer. In addition, there may be external components, which request information from or provide data to the system. The three layers can be split in various ways to tiers, which are basically separate systems that communicate in agreed, often standardized, ways. The clientserver pattern is used between the tiers and external components, the best-known case being web browser clients requesting information asynchronously and already in presentation form from web servers.

The service-oriented architecture is based on the fundamental concept of a service. Services can be simple, for example obtaining a specific resource from a resource layer, or complex, for example using an application. For using complex services one needs coordination help, which basically means instructions. This is because the same service may need to be contacted several times in order to complete the requested service. Services are described to users based on their interfaces, and a service, which has a very simple interface, may actually trigger a very complex procedure within the information system.

Fundamental architectural patterns are generic designs that employ concepts like services, events, tasks and messages. An event is an occurrence of something at a particular point in time. A task is a set of actions that need to be completed. Order of completing, control flow from one action to another, timing, cancellation of actions and other things make up workflow patterns [START_REF] Van Der Aalst | Workflow Patterns[END_REF]. A message is an object of communication. Fundamental patterns reflect common or effective ways to deal with situations and they help define generic tools, for example programming languages, event dispatchers, service catalogs, and message parsers. Typically information system designers and developers wish to use generic tools as much as possible in order to develop robust and maintainable systems.

Event-driven architecture is based on the concept of events happening at random. New events are taken up by an event dispatcher, which is responsible for routing the event to an appropriate handler or handlers. Events are handled by services. In some architectures event-handler can be added and removed at will.

The publish-find-bind pattern depicts how service providers publish services, how those needing services find them, and how the services are bound to applications or other services. This patterns implicitly assumes a shared service description language and services for publishing services. Linking of services that publish services is recursively possible by publishing them with each other.

A message broker receives messages, possibly validates and transforms them, and routes them to subscribers. An example of a message is a request for a specific service.

Architectural patterns can be exploited within a single application and they can be used as a model for collaboration among humans.

Process and workflow management

Environmental modeling is itself a process and it comprises several processes and tasks that deal with new measurements, parameter estimation and other things. These processes and tasks may be very specific and deal with established routines or they may be conceptually high-level considerations of things like what is the appropriate spatial aspect of the problem.

A workflow is a sequence of tasks, which is completed by an orchestrated use of applications or by humans. Workflow management systems originate historically in office automation [START_REF] Alonso | Web Services: Concepts, Architectures and Applications[END_REF]. A typical workflow application today is an administrative document processing system, for example a business travel procurement system. Programming languages have been developed for workflow systems [START_REF] Van Der Aalst | Workflow Patterns[END_REF]. The biggest difference between normal programming languages and workflow languages is that workflows may take days or longer to complete [START_REF] Alonso | Web Services: Concepts, Architectures and Applications[END_REF]. To cope with this workflow languages support complex recovery and exception handling routines. Workflow systems typically store the execution state into persistent storage.

Below we present two architectures, which consider processes and workflows.

Architecture for linking processes and events

A process can be thought of as anything that produces a result set at a certain point in time. A process may be passive, i.e., it waits for an operator to push data into it and run it, or it may be active, i.e. it listens to events and acts under suitable conditions. The result sets of a processes are stored into a resource layer with appropriate meta data. Each addition of a result set triggers an event, which conveys information about what kind of new information is available.

Consider for example a modeling effort, which considers storm water within a municipality. The modeling proceeds as new data is obtained. The primary processes are measurements and changes to the sensor network, engineering structures and such. Secondary processes are statistical, spatial, data cleaning and such operations, which reduce the amount of data but add to the value of new individual result sets. Tertiary processes may be models, which are run off-line but using data directly from the resource layer. Each model run produces a result set, which may be rather large but also contains appropriate meta data. Another kind of tertiary processes may be specifically designed for detecting anomalies and their results may cause events, which trigger emails or sms's.

This kind of architecture is good in the sense that it can be designed to maintain meta data that allows a high level of provenance for the modeling. The downside is that it would require rather considerable changes to current modeling practices and tools, which are not suited to such information exchange.

Architecture for formal modeling workflows

The workflow that is described in [START_REF] Jakeman | Ten iterative steps in development and evaluation of environmental models[END_REF] could possibly be coded into a workflow language. A necessary prerequisite would be an ontology for modeling tasks. The ontology should make it possible for formal treatment of stakeholders, temporal and spatial scope of the model, conceptualization, etc. Formal treatment is make possible for example by having these concepts as classes in the system. The middleware should then make it possible for storing stakeholder, conceptualization, and other such objects into the resource layer. The workflow management system for the modeling should support storing the state of the modeling project. Applications need to exist, which can be used, e.g., to create conceptualizations. The modeling workflow comprises calling such applications as unit operations. Executing the ten step modeling workflow in a specific information system would require a resource layer which can manage rather complete data about the environmental system, stakeholders and other aspects of modeling.

A shared information infrastructure in the Web implies collaboration among modeling teams, stakeholders, data providers and others. The technical implementation is greatly helped by adhering to technical standards, which also may make it easier for new parties to join. There are several architectural possibilities for developing collaborative information infrastructure, each having its specific model for collaboration. We can distinguish between information management, which in collaborative context implies shared information, and information exchange, which in collaborative context implies shared ontologies. A shared information infrastructure can employ both architectural patterns. In the first case there is a single resource layer in the space into which parties contribute, in the second case each party publishes resources into the infrastructure. Of course mixtures can exist too.

In collaborative modeling parties work on a shared deliverable and bring their own specific contribution into the collaboration. In collaborative software development one common architecture is a revision control system. There are two forms of the revision control system, which reflect the two forms of shared information infrastructure described above. In the first form a single common repository is set up and parties check out versions from that and commit changes into that. In the second form each party has its own repository, where it imports and merges changes from other repositories. Collaborative modeling can mean working on a shared model (source code) in a repository.

Social networks and various toolkit for including, e.g., geospatial applets within them have recently become very popular. The technological platform is JavaScript or other programming language embedded in a web browser. Perhaps the most advanced project employing this technology and paradigm (also called Web 2.0) is iemHUB.org. IemHUB.org is a website for developing and sharing knowledge and tools for environmental systems analysis [START_REF]Integrated Environmental Modeling[END_REF] started by US EPA.

An example of a collaborative modeling website

Within the CLIME project [START_REF] George | The Impact of Climate Change on European Lakes[END_REF] the first author was involved in a collaborative modeling effort, where several teams across Europe were using the same models but each on their own catchment and/or lake. To help this effort an interactive website was developed for uploading parameter sets for comparison. The website was developed upon a rather comprehensive project database, which contains for example used models, modeled catchments and users as classes. In the CLIME database a model object contains (links to) parameters, which are again objects, and a modeled catchment contains (links to) subcatchment objects. Although the database was not formally developed as a resource layer for environmental modeling with a unifying interface more than a standard relational database, the collaborative modeling tool was relatively easy to develop as an interactive tool exploiting the database.

The collaborative modeling web page included, besides the parameter set exchange and comparison tool, a file upload tool to share model configuration files and a simple discussion tool. No web mapping tool was included although other applications based on the modeling database were developed that included one [START_REF] George | The Impact of Climate Change on European Lakes[END_REF].

Discussion

In environmental information systems that support modeling, the resource layer contains measurement data, terrain data and data about civil infrastructure, conceptual data about the environmental system, management problem related data, and so on. In the application logic layer there are several applications for managing the data: importing new data, preparing it for analytical tools, various models, visualization tools, etc. In enterprise systems the support for, integration of, and management of these applications is a task for middleware and workflow management systems [START_REF] Alonso | Web Services: Concepts, Architectures and Applications[END_REF]. Traditionally in the environmental domain integrated modeling systems and geographic information systems (which are increasingly being integrated into modeling systems) have played a dominant role. For example the use of relational databases as independent resource layers in environmental modeling has been very limited.

The workflow language BPEL has received some attention and it has been used in environmental modeling to program a model use [START_REF] Theisselmann | Service-oriented Architecture for Environmental Modelling -The Case of a Distributed Dike Breach Information System[END_REF]. A lot is happening in the standards-based distributed data servicing due to the work carried out within the OGC. OGC has also defined a standard for a processing service (Web Processing Service, WPS), which has recently gained interest. Mainly the interest is in publishing fundamental geospatial methods as services.

The dangers in blind use of technical solutions in modeling have been said and told in numerous papers and presentations. Our findings as reported in this paper suggest that seeking for ways to publish generic modeling knowledge as services is a possibly rewarding way forward and beneficial for modeling projects. As shortly described above in chapter 3.2 the ten step procedure from [START_REF] Jakeman | Ten iterative steps in development and evaluation of environmental models[END_REF] is a potential starting point for such an exercise. At the same time possibilities for further developing standardsbased interfaces for environmental modeling databases (resource layers) and modeling middleware should be investigated.


Ulrich Bügel
email: ulrich.buegel@iosb.fraunhofer.de

Martin Schmieder
email: martin.schmieder@iosb.fraunhofer.de

Boris Schnebel
email: boris.schnebel@iosb.fraunhofer.de

Thorsten Schlachter
email: thorsten.schlacher@kit.edu

Renate Ebel
email: renate.ebel@lubw.bwl.de

Leveraging Ontologies for Environmental Information Systems

Keywords: Public Search Portals, Environmental Terminology, Semantic Technologies, Ontology Management, Ontology Mapping, Life Circumstance Ontology

The provision of accurate, comprehensive and condensed information contained in distributed environmental information systems via public search interfaces raises several technological challenges. Our approach to tackle these challenges is based on a consequent use of ontologies. Starting with an analysis of requirements resulting from semantic search scenarios, we explain the advantages of using ontologies based on standards and aim to reuse and transform terminological systems available in the environmental domain into ontologies. We develop an architecture guided by the premise of exerting a minimum of influence on existing search infrastructures. As a consequence of using a (possibly large) number of ontologies, tools for ontology management are needed. A key argument for using ontologies is that nowadaysas an outcome of the Semantic Web initiative -very powerful processing tools are available. We elaborate ontology mapping as an example and outline how a comprehensive ontology management can be achieved.

Introduction

Guided by environmental directives at the regional, national and international levels, many environmental agencies nowadays offer free access to distributed environmental information by means of public portals. Typically the underlying Environmental Information Systems (EIS) were primarily designed to provide highly detailed information and sophisticated user interfaces to thematic experts. They have to be refocused to provide information in a more restricted and aggregated form to a new user community: the public user, e.g. the citizen who wants environmental information about his surroundings. The provision of accurate, comprehensive and condensed information contained in EIS via public search interfaces is a technological challenge for several reasons: -EIS usually comprise many sub-systems which hold information in different representation forms and may have dedicated built-in search functions. When retrieving environmental information, it is helpful to link structured information in databases with unstructured information contained in documents. -For public users, keyword-based search is the most favoured method for information search. In order to integrate responses of query-based subsystems into the search results, text-to-query approaches need to be integrated. -Public users often do not know the correct search keywords that exactly match stored information which may result in a poor recall. Our approach to tackle these challenges is based on the utilization of available environmental terminologies as a basic asset. The development of terminologies has been influenced by technological progress and observed trends. The most advanced specification techniques for describing a knowledge domain are developed in the context of the Semantic Web initiative. The World Wide Web Consortium (W3C) has approved standards by which ontologies can be specified in terms of formal logic with high expressivity. In the following sections, we suggest a consequent use of ontologies and explain why this yields considerable advantages. Moreover, nowadays very powerful processing and management tools are available.

Enhancing Search Facilities -How Ontologies can Help

Semantic Search Scenarios -Why are Full-Text Engines not enough?

Nowadays, full-text search engines provide access to various types of information and exhibit excellent performance. However, they still have deficits as soon as an intelligent combination of different information types and information content is required. Users of environmental portals often search for topics with a concrete location reference, e.g. "air quality in the Upper Rhine Valley" or "measurement values for respirable dust in my environment". Moreover, users with a concrete objective in mind generally need to submit a series of requests to (one or several) EIS and collect and select "manually" the retrieved information which is relevant to take a decision. Consider a citizen who looks for "building land" where he would like to build a house. He is interested in getting an overview about environmental conditions (e.g. transport connections, noise disturbance, air quality, land contamination) in a given geographical area. An ontology describing life circumstances may define a concept structure "build house"/"find location"/"living conditions"/"environmental conditions" that helps to provide links to all content of interest as the result of a single search. The idea is to use keywords entered by a user to identify corresponding concepts defined in an uploaded ontology. Starting from a concept, other concepts that reside in a "semantic bounding box" determined by ontological relationships (parent, children and property relationships) are additionally looked up in the ontology. This context can be used to provide automatic query expansion and contextbased navigation through domain knowledge before submission of the final query.

Architectural Aspects -Spicing up the Established Search Infrastructure

Technologies emerging in the Semantic Web area still have some academic flavour and their integration into established infrastructures for conventional information retrieval is a challenge. In order to achieve acceptance by organisations that operate an EIS, we favour an approach by which components of the deployed IT infrastructure (e.g. catalogues, search engines) do not need to be replaced. Hence, we propose an architecture which foresees semantic add-ons in terms of pre-processing of user queries before their execution through the deployed EIS search facilities, and post-processing of search results before their final presentation to the user. The architectural approach is outlined in Fig. 1. We distinguish three main architectural components: the search portal, the target systems in the EIS and the Ontology Management System providing the semantic add-on functionality.

In the first step, search strings entered via the portal are processed by a text-toquery system. In order to resolve relationships between the input text in natural language with ontological concepts, we suggest to use automatic semantic annotation tools as described in [START_REF] Bügel | Discovery and analysis of environmental information based on formalised terminology[END_REF]. Once corresponding concepts have been identified, queries to the ontology store (e.g. SPARQL queries) can be composed automatically. These queries return the "semantic bounding box" of the identified concepts. The portal software transforms this ontological structure into a navigation structure which allows the user to explore the semantic context of his query and then issue his request more precisely, e.g. by narrowing or widening his initial request.

In the next step, the portal software constructs requests to the various EIS target systems. As the ontological elements have been defined independently from the interfaces of the target systems, we face another matching problem here; this can be resolved by attaching target system information to the ontological elements. This crucial issue is not further elaborated here; a generic approach based on OpenSearch descriptions is described in a further contribution to ISESS 2011 [START_REF] Schlachter | Towards a Universal Search in EIS[END_REF].

Once the results from the target systems have been received by the portal, ontology based post-processing can be applied before the results are finally presented to the user. One way to improve the quality of the search results is to exert influence on the ranking of the results by means of the ontology, i.e. the results are ordered according to their semantic closeness to the search item. For instance, if a user searches for "environmental conditions", entries about "air quality" are ranked higher despite the fact that the terms "air" and "quality" were not search terms [START_REF] Madlener | Entwurf und Implementierung eines Verfahrens für Ontologie-basiertes Ranking von Suchergebnissen[END_REF].

Finally, the search results from target systems with various forms of information representation (e.g. text-based systems, databases) can be presented uniformly and combined by means of relationships defined in the ontology.

Ontologies -Where do they come from?

We have validated the developed architecture in various projects, e.g. as frontend/ backend of conventional OGC Catalogue Systems [START_REF] Hilbring | Ontology-Based Discovery of Geoscientific Information and Services. Session on Semantic Interoperability[END_REF] and in a project "Semantische Suche nach Umweltinformation (SUI)" of the German environmental cooperation network KEWA [START_REF]F+E Vorhaben KEWA Phase V 2009/10[END_REF] for an integrated semantic search over environmental systems in Baden-Württemberg [START_REF] Abecker | SUI -Ein Demonstrator zur semantischen Suche im Umweltportal Baden-Württemberg[END_REF][5] [START_REF] Schlachter | Towards a Universal Search in EIS[END_REF]. Here, we have made two important observations: -We usually have to deal with a number of ontologies from different sources, rather than a single ontology covering all aspects. -As ontology development is costly and time consuming, whenever possible ontologies should not be developed from scratch. Especially in the area of EIS, the information to be covered in many cases is available in terms of terminological systems which have formerly been developed under huge effort. Thus, we favour to reuse these systems and spend new effort mainly for development of methods and tools which transform these systems into ontologies and which help to automate the process of updating. In SUI, we use the following ontologies: -Environmental thesauri: We use GEMET (GEneral Multilingual Environmental Thesaurus [START_REF] Gemet | [END_REF]) because it covers a comprehensive base of search terms. GEMET is available as SKOS ontology (Simple Knowledge Organisation Systems [START_REF] Webgenesis | [END_REF]). -Feature Type Catalogues: In order to facilitate data exchange in integrated EIS such as WIBAS [START_REF] Wasser | Arbeitsschutz (WIBAS[END_REF], so-called feature type catalogues are being defined for all domain areas represented by features that describe real word phenomena. This information is often available as structured data in environmental databases.

Although not originally designed for the task of keyword based search, such catalogues provide valuable information, especially for accessing structured information (e.g. object type codes). We have transformed the WIBAS feature type catalogue into a SKOS ontology such that it can be used in the search and navigation interface. -Life circumstance ontologies: We have referred to this in the "build house" scenario in section 2.1. Portals of various federal state authorities in Germany offer a navigation through their web pages according to a structure of defined life circumstances, e.g. "Service-BW" operated by the Ministry of Interior of Baden-Württemberg [19] or the "Directory to Administrations" operated by the Bavarian Ministry of Interior [2]. We have extended the structure of Service-BW with concepts that specialise on environmental information issues and made it applicable to semantic search. The list is open, i.e. there are plans to transform further terminological systems into ontologies in order to feed the semantic search interface. A candidate is the GSBL (Gemeinsamer Stoffdatenpool Bund/Länder), a database of substances operated by the German Federal Government and the Federal States [START_REF]Gemeinsamer zentraler Stoffdatenpool Bund/Länder (GSBL[END_REF].

Fostering Integrated Domain Knowledge -Ontology Mapping

Up to now, we have explained that ontologies can guide users to explore available domain knowledge. Naturally, each of the deployed ontologies offers specialised knowledge focussed on the common view of a particular domain. Cross references between domains are not explicated and the terms used to express similar concepts in different domains may differ. However, the ontology system should provide means to make these relationships transparent to the user.

The necessity for ontology mapping arises because query expansions computed for search terms should have a range across multiple ontologies. Consider for instance a search for "building land": the corresponding concept is found in GEMET and a bounding box will be constructed only from ontological elements contained in GEMET. If we relate the concept "building land" in GEMET to the concept "build house" as described above, the bounding box will additionally contain ontological elements from the life circumstance ontology.

Such mappings can be constructed by means of automatic ontology mapping tools. In the SUI project, we use a tool developed in the German Research Program THESEUS [START_REF]The THESEUS Programme[END_REF] which utilizes Particle Swarm Optimization (PSO) to search for the optimal alignment of ontologies [START_REF] Bock | Discrete particle swarm optimization for ontology alignment[END_REF]. It was specially designed for parallel execution in distributed systems and exhibits excellent performance especially for large ontologies.

While tools for automatic ontology mappings are available (e.g. in the form of console programs) and can be selected according to specific needs, integration of these tools into a concrete application environment requires additional functionality and customization. Bilateral mappings between ontologies have to be developed and deployed. Moreover, it is important to provide means for manual post-processing of automatically generated mappings; although they can be very helpful and some tools exhibit excellent precision/recall, we do not recommend uncritical confidence in these automatic findings and we recommend to check the consequences in the context of the entire application. Fig. 2 illustrates a workflow for the management of mappings as applied in the SUI project. Via the administration GUI of the SUI ontology system mapping contexts for bilateral mappings can be defined and stored. For a stored context, a run of the automatic mapping process can be configured and started. It produces an alignment, i.e. a number of correspondences between entities of two ontologies in the format of the Alignment API [START_REF] Euzenat | An API for ontology alignment[END_REF]. Dependent on the size of the ontologies, this process may consume a considerable amount of time. The computed alignment is treated as an initial suggestion of correspondences which can be manually post-processed via the GUI: correspondences can be accepted, rejected, added and changed. Finally, the alignment can be activated. Activated alignments are involved in the execution of the SPARQL queries: the resulting semantic bounding is spawned across ontologies.

Driving the Change -A Comprehensive Ontology Management

We have elaborated the aspect of ontology mapping as we conceive it as a key component to support semantic search scenarios. Beyond that, ontologies can provide excellent support for further applications needed in EIS, e.g. navigation in content, interpretation and analysis of content, visualization of relationships or combination/orchestration of environmental information services. The effective utilization of the expressive power of ontologies can be further extended by consecutive incorporation of ongoing developments of domain ontologies, geoontologies, top level ontologies and ontologies constructed from any available representation format. This can open up outstanding opportunities to excavate treasures of information hidden deeply in a distributed EIS.

However, there are no benefits without costs: the boosting impact of ontologies can only be leveraged if we can provide the means for a comprehensive ontology management. One important motivation to favour ontologies as the unique representation form is that the Semantic Web is a driving force not only for standardization, but also for the development of the needed management tools. Ontology management covers a wide range of tools, namely for ontology design, evolution, persistent storage, querying, mapping, reasoning, visualization and provenance management. Moreover, there is a growing number of available tools to extract instance data from various sources such as databases, text-based systems, web pages, media, sensors systems or maps.

In the SUI project we mainly integrate tools developed in the context of the THESEUS program. The THESEUS Core Technology Cluster (CTC) focuses on the development of generic components to be used in several defined use cases. Especially CTC-WP3 is concerned with all aspects of ontology management. At Fraunhofer IOSB we integrate tools developed in WP3 (and other WPs) into our development platform WebGenesis® [START_REF] Webgenesis | [END_REF]. We apply it in the shape of an Internet ontology workbench in several projects in the area of EIS. In SUI, we have reused and customized the following components: -HARMONIA, the THESEUS component for ontology mapping (cf. section 3).

-MNEMOSYNE, a component which addresses the fact that keeping large ontologies in main memory is no longer adequate and an OWL based alternative to the persistent storage in RDF triple stores is needed. It provides a persistency layer for ontologies based on the de-facto standard OWL-API [START_REF]OWL API[END_REF]. This standard is used in the well known ontology development tool Protégé in its version 4. MNEMOSYNE has been integrated into Protégé [START_REF] Henß | A Database Backend for OWL. 5th Int. Workshop on OWL: Experiences and Directions (OWLED 2009)[END_REF]. -PYTHIA, which can convert SPARQL queries into the query language of the OWL-API. We have customized PYTHIA by adding a front end for graphic based construction of queries and integrated it into the ontology workbench.

We plan to integrate further CTC components into the SUI ontology system. The near-term focus will include components for text-to-query conversion, disambiguation of geo-spatial references [START_REF] Kleb | Ontology based Entity Disambiguation with Natural Language Patterns[END_REF], reasoning brokerage [START_REF] Bock | A Reasoning Broker Framework for OWL. 5th Int. Workshop on OWL: Experiences and Directions (OWLED 2009)[END_REF] and integration of growing pools of web data ("Linked Open Data" [START_REF] Haase | The Information Workbench -Interacting with the Web of Data[END_REF]).

Conclusions

Finally, it can be stated that semantic search technologies and ontology management tools can help to "open-up the door" for public users to information contained in EIS which was originally designed for domain experts. For implementation purposes we recommend to use technologies, tools and standards (e.g. OWL-API, Alignment API, SPARQL) developed in the context of the Semantic Web initiative. Deployment of innovative tools within an existing EIS infrastructure, even when being state-of-theart and easily available, requires careful selection, adaptation and operation in order for them to be accepted by the EIS community.

Fig. 1 .

 1 Fig. 1. Pre-and post-processing of keyword based queries via public portals by an Ontology Management System.

Fig. 2 .

 2 Fig. 2. State Transition Diagram for a Mapping Workflow.

Acknowledgements. The presented work has been funded by the German Federal Ministry of Economics (BMWi) in the THESEUS research programme under grant 01MQ07019 and by the environmental research network KEWA led by the Ministry of the Environment, Nature Conservation and Transport of Baden-Württemberg, Germany.


Niclas Hjerdt
email: niclas.hjerdt@smhi.se

Berit Arheimer

Göran Lindström

Ylva Westman

Esa Falkenroth

Martin Hultman

Going Public with Advanced Simulations

Keywords: Large scale, High-resolution, WFD, public access, web interface

de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

The new challenges of water management

Hydrologic modelling has traditionally been an exclusive activity for trained hydrologists, but this has begun to change rapidly as a result of recent development in technology and legislation. The implementation of the European Union Water Framework Directive (WFD) has created a need for hydrologic information at high resolution in both time and space. In addition, web technology now provides tools to efficiently share vast volumes of data. Other recent legislation at the EU level, such as the INSPIRE Directive, has created a framework for sharing geographic information with the public in a standardized manner.

At 450,295 square kilometres, Sweden is the third largest country in the European Union by area, and Sweden's 23,418 reported water bodies make up a relatively large proportion of all European water bodies. Since Sweden is a country Niclas Hjerdt, Berit Arheimer, Göran Lindström, Ylva Westman, Esa Falkenroth, and Martin Hultman with vast water resources and a relatively small population, it is impossible to maintain a monitoring program that covers all water bodies. Therefore, models offer a cost-efficient option for many steps in the six-year water management cycle. Therefore, the implementation of the WFD and subsequent delineation of water bodies has created a need for models that can operate at a much finer resolution than previously needed. This could not be achieved with, for instance, the hydrological model used in the international reporting of nutrient loads to the Baltic, i.e., the HELCOM Pollution Load Compilation. The model used for this is the HBV-NP model which only divides Sweden in around 1,100 subbasins, as compared to the 23,418 water bodies reported to the EU (Figure 1)..

The solution, part one: A new, large-scale hydrologic model with high resolution

The challenges of the WFD clearly created a need for a new type of hydrologic models. One such model is the hydrological catchment model HYPE (HYdrological Predictions for the Environment), which simulates water flow and substances on their way from precipitation through soil, river and lakes to the river outlet [START_REF] Arheimer | Development and test of a new Swedish water quality model for small-scale and large-scale applications[END_REF][START_REF] Donnelly | An evaluation of multi-basin hydrological modelling for predictions in ungauged basins[END_REF]. The catchment is divided into subbasins which in turn are divided into classes (calculation units) depending on land use, soil type and elevation.

An important feature of HYPE is that classes can not be coupled to a geographic location within the subbasin but are given as part of its area. Typical land uses are forest, lake, open land, but also different crops, e.g. cereal and potatoes, are common. Elevation can be used to get temperature variations within a subbasin to influence the snow conditions.

In contrast to the earlier type of models, such as the HBV-NP model, HYPE simulates the water flow paths and nutrient transformations in soil. The soil is modelled as several layers which may have different thickness for each class. Agricultural land classes commonly use three soil layers to simulate nutrient transformation more accurate. Model parameters may be associated with land use (e.g. evapotranspiration), soil type (e.g. water content in soil), or be common for the whole catchment. This way to couple the parameters to geographic information makes the model better suited for simulations in ungauged catchments.

HYPE simulates the nutrients nitrogen and phosphorus divided into the following fractions: inorganic nitrogen, organic nitrogen, soluble reactive phosphorus and particulate phosphorus. In addition, organic material and conservative substances like 18 O can be simulated. The calculations are made with a daily time step for water and concentrations, but result can also be delivered as mean values over a longer period and in the form of transported amount of nutrients and source apportioned transport. The HYPE model is set up for a growing number of basins, including Sweden [START_REF] Donnelly | An evaluation of multi-basin hydrological modelling for predictions in ungauged basins[END_REF], the Baltic Sea drainage basin, Europe [START_REF] Donnelly | An evaluation of multi-basin hydrological modelling for predictions in ungauged basins[END_REF][START_REF] Strömqvist | Using recently developed global data sets for hydrological predictions[END_REF], and La Plata in South America.

The solution, part two: Storing data in an efficient and flexible database

Running the HYPE model at high resolution for a country or a continent quickly generates vast volumes of data. For Sweden only, the present S-HYPE version with 17,313 subbasins and calculations of discharge, nitrogen-and phosphorous transport with a daily time step for the period 1990-2010 generates approximately 400 million values. For Europe, the present E-HYPE version with 47,000 subbasins generates approximately half a billion values of daily discharge values.

The best solution in our opinion was to follow standard procedure. As a first step, a database model was developed to characterise the dependencies and relationships between different attributes to be stored in the database. After the structure of the database had been determined, a Postgres 8.4 database was generated. A database loading program in Java was developed to read model output files into the SQL database.

It was crucial already at this point to assure that the database would be highly accessible through a web-based interface. Achieving short response times was a priority. Different accessibility solutions were developed for the Swedish database and the European database. For the Swedish database, data were cached in main memory (currently 68 GB main memory) for rapid delivery. For the European database, we chose to partition the database per subbasin, which resulted in a good throughput as long as you retrieve data from a limited number of subbasins. The partitioning require much less main memory on the database server but the disadvantage is sacrificed flexibility of retrieval, i.e., retrieve all subbasins for a single point in time is not practical.

Niclas Hjerdt, Berit Arheimer, Göran Lindström, Ylva Westman, Esa Falkenroth, and Martin Hultman

The solution, part three: A web-based user interface

Developing a web-based user interface was a strong requirement to assure public accessibility. We have developed two services that satisfy a range of data accessibility needs.

The first service is a simple but functional map interface which allows users to first identify and select subbasins of interest. After subbasins have been selected the user fills out a short form to identify the data period, temporal resolution and variables to download. Finally, after the "download" button has been pressed, data series will be delivered in a spreadsheet that can be opened or saved to a local hard drive.

The second service is a web service that delivers data in xml-format following a REST call. This solution is suitable for machine-to-machine communication and when downloading data for a large number of subbasins. Due to network security reasons, the web service has so far been restricted to registered users, but there are plans to turn this into an open API in the near future. Technically, we have identified a series of components that together meet the requirements of effective, web-based, data-sharing (Table 1): The web-based user interface is constructed with the framework Google Web Toolkit (GWT) together with Openlayers as a map viewer. With the help of GWT both client and server side can be written in java-code. On the server side, JDBC is used to communicate with a Postgres database and Apache POI generates spread sheets from returned data. The web services REST interface is built with the help of Jersey. Both the web-application and web services is hosted within a Tomcat web server that runs under Apache. Geoserver and GeoWebCache is used to host to the Openlayers client with geographical metadata and pre cached maps through standard protocols WFS and WMS.

The solution, part four: An operational production line for WFD support services

In most cases, piecing together models, databases and web pages is carried out in project form with financial resources restricted to a limited time period. However, to meet the challenges of European water management, information must be updated and accessible more or less continuously. One solution is to incorporate supporting systems into an operational production line, but this requires a solid technical infrastructure.

Fig. 2.

A simplified illustration of the operational production line for WFD supporting services at the Swedish Meteorological and Hydrological Institute. Parts of this production line is also utilized for our pan-European services, such as E-HypeWeb [START_REF]Discharge in European rivers according to the E-HYPE model[END_REF].

This technical infrastructure is already in place at SMHI. As a government agency with national responsibility for meteorology, hydrology and oceanography, SMHI already operates a number of services on a daily basis. There is a strong IT department with high computational capacity and around-the-clock technical support. Data security issues are dealt with in a professional manner. The production line for WFD supporting services has now been established at SMHI but is still in its infancy and subject to improvements (Figure 2). Due to Sweden's late implementation of the WFD (2004), and even later involvement of Niclas Hjerdt, Berit Arheimer, Göran Lindström, Ylva Westman, Esa Falkenroth, and Martin Hultman WFD at SMHI (2008), many WFD supporting services were initially developed as individual projects with minimum overlap. As a result, these systems became very expensive to keep updated and operational. Therefore, many first-generation systems are now being merged to maximize cost-effectivness.

The establishment of a production line is critical also for handling system and information updates efficiently. For data on river discharge and nutrient transport, both products of multiple data sets and calculation steps, it is critical to keep track of the exact combination of model versions and data sets used in the calculation. Nevertheless, new observations, or changes in the delineation of water bodies, require databases and models to be updated frequently. Thus, data sets must be carefully associated with metadata to ensure that data can be traced and reproduced at a later stage. In our opinion, it is very difficult to sustain the handling of data versions and reproducibility without a firm production line in place.

Conclusions

The first web application that gave public access to large-scale hydrologic simulations with high resolution was SMHI VattenWeb [START_REF]Discharge and nutrient transport in Swedish rivers according to the S-HYPE model[END_REF], a web interface that offers time series of river discharge and nutrient transport from 17,313 subbasins in Sweden. Since the launch in April 2010, the number of unique visitors has steadily increased and approach 3000 at the end of March 2011 (Figure 3). Later during 2010 a pan-European HYPE model, E-HYPE, was set up and launched as a demonstration tool [START_REF]Discharge in European rivers according to the E-HYPE model[END_REF]. The E-HYPE model has a resolution of approximately 47,000 subbasins across the European continent. Other publicly available web services include Balt-HYPE, a HYPE model for the Baltic drainage basin [START_REF]Discharge and nutrient transport for the Baltic drainage basin according to the Balt-HYPE model[END_REF].

We believe that strong hydrologic research and development must be placed in an operational production line to satisfy the needs of the WFD. We have demonstrated how a production line has been set up at the SMHI and described the different components. It is clear that public interest for these services is very high and we see many benefits of such systems at the European scale, not only for fresh water management related to the WFD, but also in other sectors. In the near future we will probably see the emergence of a large variety of downstream services that utilize data from publicly available core services.

Fig. 1 .

 1 Fig. 1. The resolution of models used in the HELCOM pollution load compilation reports (left) and for the EU Water Framework Directive reports (right).

EFig. 3 .

 3 Fig. 3. Number of unique visitors at SMHI VattenWeb [5] from the start in April 2010 to March 31, 2011, according to statistics from Google Analytics.

Table 1 .

 1 List of reliable components that have been used to construct the technical basis of SMHI VattenWeb.

	Purpose	Component
	Web-application framework	Google Web Toolkit
	Client side map viewer	Openlayers
	Database solution	Postgres 8.4
	Coupling to database	JDBC
	Log	log4j
	Map server	Geoserver and GeoWebCache with cached images
		for different zoom levels
	Web server	Apache
	Run web applications in Java	Tomcat
	Web service call	Jersey
	Spread sheet generation	Apache POI


Karel Kisza
email: kkisza@mail.muni.cz

Jiří Hřebíček

SISE Semantics Interpretation Concept

Keywords: Semantics, SISE, ontology, expert system

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The paper presents a semantic idea of interpretation of theoretical model of the Single Information Space for the Environment in Europe (SISE). The Hrebicek and Pilman theoretical conceptual model [START_REF] Hřebíček | eEnvironment and the Single Information Space in Europe for the Environment[END_REF] was compared with an upper ontology concept of the SISE [START_REF] Hřebíček | Conceptual Model of Single Information Space for Environment in Europe[END_REF].

The developed conceptual model of the SISE enables an implementation of the vision of development for an integrated, modern, common, shared and sustained Single European Information Space infrastructure for environmental information exchange and environmental management in Europe. The upper ontology concept of SISE was not suitable for more specific tasks (e.g. for the implementation of effects of environmental information), where the detailed level of the solution is needed. In this case it was necessary to use a more detailed model, which is much more specific than the upper ontology model (European ICT Environmental Sustainability Research, 2010) [START_REF] Pillmann | Information Sources for a European Integrated Environmental Information Space[END_REF], [START_REF] Hřebíček | eEnvironment and the Single Information Space in Europe for the Environment[END_REF].

Currently the research and development of shared environmental information system [START_REF] Pillmann | Information Sources for a European Integrated Environmental Information Space[END_REF] focuses on the improvement of search results by using semantic web technologies [START_REF] Pariente | A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic Framework[END_REF]. Internet users without special knowledge in environmental topics should be enabled to find all available information for their special requirements by placing a request as simple as possible.

There is a big amount of various kind of ontological model in the environmental research area. These models are built both for systems in various fields (domain ontology) and for systems in business companies (application ontology). Domain ontology defines a domain based set of terms and relations, which should be independent of any problem solving method. Application ontology contains terms and relations unique to particular problem solving method. This customization allows to separates out control and procedural knowledge. The use of pre-defined domain ontologies enable users to reduce the available search space. Ontological concepts relevant to a problem domain are supplied to the user allowing them to focus their query. A more advanced interface would allow the user's query to use their terms and map that to an underlying domain. The big challenge is to find the best way how to make ontology semantics available to the user using appropriate problem solving (search) interface. Problem solving methods are defined as reasoning strategies for solving certain problem types. Here expert systems come to be used.

Since their appearance in the 1970's, rule based expert systems have found applications in a variety of domains ranging from control systems for power plants to help systems. They are mainly used for tasks like diagnostics or predictions. The typical scenario consists of interviewing the user in order to acquire facts that are used afterwards to generate the predictions or diagnostics. Many common expert systems have a reduced number of observable facts and the intelligence of the system is used only for the process of generating the prediction [START_REF] Merritt | Building Expert Systems in prolog[END_REF]. Most of them do not take into consideration the importance of this set of observable facts. The limited number of observable facts induces a generality problem that affects the quality of the observable facts.

Experts systems are capable of performing complex tasks in their specific field of expertise. They use a vast amount of explicit and tacit domain knowledge -domain ontology which should support problem solving method. A problem solving method is called an interface engine (in expert systems terminology).

The paper tries to show the idea of ontology semantic interpretation and related summary of used technologies, based on modern expert systems combined with ontological models platform. This concept combines main advantages of ontologies and expert systems -the ontology semantics and the rule based application used for searching information resources and issuing predictions similar like in [START_REF] Pariente | A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic Framework[END_REF].

Expert system vs. ontology

The definition of ontology varies in the literature. A common understanding the ontology represents knowledge about the real word domain. The expert system is the system containing information about the problem domain in its knowledge base. Both of them are usable for knowledge management (defining, storing, searching, etc.). The common understanding of an expert system and ontology is very similar. Therefore, as the first step it is necessary to describe their definitions, their main characteristics and comparison.

Expert system

Expert systems are mainly computer application which enables both algorithmic and non-algorithmic expertise for solving particular types of problems. Basically they are composed from four basic parts as is seen in Figure 2:

• Knowledge Base -declarative representation of the expertise represented in IF … THEN … rules; • Working Storage -the data that is specific to problem being solved;

• Inference Engine -the heart of the system which derives recommendation from the knowledge base and working storage; • User Interface -component for input queries from users. Expert systems recognize three basic types of knowledge:

• Descriptive (conceptual) knowledge (stored in knowledge base) -describes the concepts in the given domain and the relations among them. Each defined concept should be described by relation to already existing concepts. • Procedural knowledge (stored in knowledge base) -clearly describes the procedures (actions) which should be processed in particular situation • Factual knowledge (stored in working storage) -means the facts formalization. Especially facts describing particular situation or problem which should be solved

Ontology

Very precise definition of ontology in the context of computer and information science has been written by Tom Gruber [START_REF] Gruber | Encyclopedia of Database Systems[END_REF]: "An ontology defines a set of representational primitives with which to model a domain of knowledge or discourse. The representational primitives are typically classes (or sets), attributes (or properties), and relationships (or relations among class members). The definitions of the representational primitives include information about their meaning and constraints on their logically consistent application." In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals.

Expert system and ontology combination advantages/disadvantages

It is necessary to accept the limitations of the technology upon which current knowledge-based systems are built. An inference engine alone doesn't provide an expert system. The main parts of each knowledge based systems are knowledge. As was mentioned in above text the limited number of observable facts induces a generality problem that affects the quality of the observable facts. Existence of many huge ontology models in various areas, their complexity and tolls supporting their development help to solve the lack of observable facts in expert systems based on ontology.

Moreover, it is usual in practice that two systems are using the same terms which mean different, sometimes completely contradictory, things. Ontology can help to pin down precisely what these differences are. More than serve as meta-data model, ontology can help to aggregation of more existing expert systems without loose of interoperability.

One of the tasks which have to be solved concerning SISE is to find the best way how to make ontology semantics available to the user using appropriate problem solving (search) interface. Experts systems are capable of performing complex tasks in their specific field of expertise, which should help to solve this task.

Ontology based Expert system

As was stated in above text, the knowledge base should be the (SISE) ontology (mainly implemented in OWL). The main architecture concept of expert system based on ontology can be seen in the Figure 3.

Fig 3. Expert system based on ontology concept

The Ontology Knowledge&Working Base is in fact the ontology model transformed to expert system knowledge and working base. The idea of transformation is described in the next part. The Inference Engine is the mechanism which applies the axiomatic knowledge present in the knowledge base to the problemspecific data leading to the conclusion. The User interface component is responsible for communication between user and expert system.

Idea of implementation

For implementation idea will be used JESS the Rule Engine for the JavaTM Platform as main implementation language [START_REF] Rowl | Rule Language in OWL and Translation Engine for JESS[END_REF]; ROWL -Rule Language in OWL and Translation Engine for JESS [START_REF]JESS -the Rule Engine for the JavaTM Platform[END_REF] to transform ontology to expert system rules; and ontology written in OWL.

As the first step is it is necessary to transform ontology (written in OWL in this case) to the knowledge and working base for expert system written in JESS. It can be done by using ROWL. It enables users to frame rules in RDF/XML syntax using an ontology in OWL. Using XSLT stylesheets, the rules in RDF/XML are transformed into forward-chaining defrules in JESS. The main principle of using ROWL can be seen on the Figure 4. Fig. 4. OWL inference engine using XSLT and JESS -overall architecture [START_REF] Rowl | Rule Language in OWL and Translation Engine for JESS[END_REF] Whole ontology model is transformed using style sheets (XSTL Engine) to ontology usable in JESS and can be used in expert system.

Conclusion

There was discussed the concept of combination of expert system and ontology in the paper. This concept is based on classical expert system architecture, where knowledge and working base is based on ontology. It has been discussed advantages and disadvantages of such combination and possibilities of this approach contribution to solve SISE task concerning finding the best way how to make ontology semantics available to the user. Finally, there were described the idea of implementation of this concept using new technologies (JESS, OWL, ROWL).

Fig 1 .

 1 Fig 1.Hrebicek and Pilman concept of SISE[START_REF] Hřebíček | eEnvironment and the Single Information Space in Europe for the Environment[END_REF]

Fig 2 .

 2 Fig 2. Expert system schema

Karel Kisza1, Jiří Hřebíček2,