Tomáš Ludík

Jaroslav Ráček

Process Methodology for Emergency Management

Keywords: Process Management, Emergency Management, Process Methodology, Recommendations

Emergency management is represented by managerial functions which aim to reduce vulnerability to hazards and to cope with disasters. Emergencies often have direct impact on the environment. This paper focuses on identification and subsequent software support processes in the emergency management. The paper aims to describe a process methodology for emergency management in details. The methodology describes how to manage an information system development suitable for emergency management, which is built on business processes. The methodology consists of five main phases. Each phase is described in terms of individual activities, work products, and user roles. The next part of the paper recommends the use of particular technologies, tools and resources that have been successfully proved in the analysis of emergency situations in the Czech Republic. The straightforward outcome of the novel process methodology is more effective solution of emergency situations and therefore the reduction of negative environmental impacts.

Introduction

The emergency management requires considerable effort. To manage emergencies, it is necessary to spend human resources and also technical resources. It is useful if there are available some best practices in solving the emergencies, and also specified responsibilities for particular activities. To capture such information it is appropriate to use the process management, which has been approved in the private and public sector [START_REF] Kubíček | Process Support and Adaptive Geovisualisation in Emergency Management[END_REF].

Identification and subsequent automation of the process is a challenging issue. At present, there are primarily two different approaches to the business process deployment. The first approach is based on the business process life-cycle, the second one on the overall architecture which supports the business process deployment. Both of these approaches are supported by the multinational corporation (Object Management Group and Workflow Management Coalition), which enforces standards in this field.

It is convenient to integrate the above mentioned approaches to the process management for effective process deployment and create a common framework for an unified view of the process deployment. Such a view is defined by Process Framework for Emergent Management that takes into account specific factors relating to the emergency management [START_REF] Ludík | Process Support for Emergency Management[END_REF].

The Process Framework for Emergency Management provides a basic view of the process deployment from the methodology and architecture point of view. For this reason, this paper aims to describe the process-oriented methodology in detail, which is an essential part of the framework.

Methodologies Based on Business Process

There are many methodologies that lead the user through the process deployment, e.g. Object Process Methodology, Rational Unified Process, Methodology for Modelling and Analysis of Business Process or Business Driven Development. The business process analysis is the principle of these methodologies, but the process automation of does not follow the process management ideology. The next part of the paper briefly describes the methodologies and also shows their disadvantages.

Object Process Methodology

Object Process Methodology (OPM) [START_REF] Dori | Object-Process Methodology -A Holistic Systems Paradigm[END_REF] is an approach to designing information systems by depicting them using object models and process models. OPM combines a minimal set of building blocks with a dual graphic-textual representation in a single diagram type. This makes OPM formal yet accessible to systems engineers and other stakeholders, enabling them to be involved through modelling from the early stages of requirements formulation.

The main disadvantage of this approach is the non-standard diagram notation, which has similar properties to the Data Flow Diagram (DFD). Another disadvantage is a low correlation between modeled process diagrams and their subsequent implementation.

Rational Unified Process

The Rational Unified Process (RUP) [START_REF] Shuja | IBM Rational Unified Process Reference and Certification Guide: Solution Designer[END_REF] is an iterative software development process framework created by the Rational Software Corporation, a division of IBM since 2003. RUP is not a single concrete prescriptive process, but rather an adaptable process framework, intended to be tailored by the development organizations and software project teams that will select the elements of the process that are appropriate for their needs.

The main disadvantage of RUP methodology is that business process analysis is used only at the beginning in order to create business requirements. The final application reflects the business processes, but there is not created a closer bond with them. Therefore, even a small change of business process leads to a fundamental change of the created information system.

Methodology for Modelling and Analysis of Business Process

Methodology for Modelling and Analysis of Business Process (MMABP) [START_REF] Řepa | Podnikové procesy, procesní řízení a modelování[END_REF] is based on the fact that a business process model is next to an object model one of the two key assumptions of the information system global model. Both of these models form the basis of a business world general model. The object model is a static model of reality. In contrast, a process model is dynamic, and therefore describes the transition from initial to final states of the process.

The advantage this approach is the use of BPMN for process diagrams and Class diagram (UML) for describing objects. The disadvantage of this methodology is its focus on process analysis and modelling. The methodology therefore does not cover the entire business process life-cycle.

Business Driven Development

Services-Oriented Architecture (SOA) provides an IT framework along with a set of principles and guidelines to create IT solutions as a set of reusable, composable, and configurable services that are independent of applications and runtime platforms. Transitioning an enterprise to SOA requires a Business Driven Development (BDD) approach that uses business goals and requirements to drive downstream design, development, and testing [START_REF] Mitra | Business-driven development[END_REF]. It brings a much needed flexibility in enterprise IT and helps to align IT solutions with business needs.

It is one of the best so far described methodologies from the view of the close interdependence to the business processes. But there is missing the application of a workflow reference model that allows to deploy the modelled process instance directly to the workflow engine.

Process Oriented Methodology

The created process-oriented methodology is based on the above mentioned methodologies but also eliminates their disadvantages. This innovative methodology is described in terms of different phases, from which it is composed, as well as in terms of responsible user roles and work products generated by this methodology.

Phases

The first phase of the methodology is Identifying. It tries to define the main strategic objectives of the organization. In accordance with the objectives the processes that bring added value to the organization are identified, directly or indirectly. Accordingly, these processes can be divided into primary, support and management processes [START_REF] Fiala | The Model of Process Framework in Czech Public Government[END_REF]. It is also appropriate to assign responsibility for individual processes and particular activities as well. It is appropriate to use the Use Case Diagram to integrate the processes and user roles. The output of the phase is a list of business requirements (more details are in the section 3.3). It is convenient to define the Glossary to better understand the area of interest, which facilitates communication between user roles. The last tasks of the phase are verification and validation of business requirements.

In the Modelling phase, the business process is in detail modelled and decomposed into several levels, depending on its complexity. During this phase the emphasis is on the correct data flow in processes. Decision-making in processes are solved by using business rules, which could be changed during the process runtime. Designed process should be simulated in this phase. Simulation can reveal bottlenecks in the process and also visualize the process functions. The outcome of this phase is the system requirements determination that are in accordance with business requirements. System requirements should be approved by the user-validation. One of the key elements in this phase is the appropriate automation level determination.

Configuration is the third phase and deals with a detailed set up of business processes. Processes are transformed from the modelling phase into the configuration phase mostly in BPEL. In this form they are accompanied by the necessary functionality build on service-oriented architecture. Processes consist of already existing services or of the brand new ones that need to be programmed. In this phase are created the key performance indicators that are intended for the process performance control during the runtime. In such way the comprehensive application is built on business process. Its instances can be deployed on the workflow engine. The created system is set to the end customer. The service and process testing, as well as system validation with respect to the system requirements belong to the control mechanisms of this phase.

Execution/Monitoring phase provides primarily two activities. First it is an administration of running process instances in the workflow engine. That allows the end users to effectively work with the processes. Created applications can be set up and configured during the runtime. Business rules enable the configuration of the branching in processes, which enables better response to possible changes in a company. Setting user rights and roles is another option. Roles and rights can be assigned or removed for the current or new users, according to their current responsibilities. This phase is also responsible for process monitoring and for gathering data about process run. Based on this information, it is possible to evaluate the process progress and partly adapt the process on the flow. Defined KPIs have a great impact. They enable overall control of the process and therefore also a rapid response to sudden changes.

The last phase is Optimization. This phase is crucial for continuous process improvement. During the monitoring phase the data about process instances are collected, which may result in some gaps in the modelled process. There are some advanced techniques of mathematical statistics or process mining available for the process instances analysis. Based on the results it is possible to choose two different approaches to process improvement. It is a Business Process Reengineering (BPR) or a Total Quality Management (TQM) [START_REF] Řepa | Podnikové procesy, procesní řízení a modelování[END_REF]. TQM is focused on the consequent improvement of processes. On the other hand, BPR focuses on radical changes. According to the chosen approach, new specification of business processes is created.

Roles

Stakeholder represents interest groups whose needs must be satisfied by the project. It is a role that may be played by anyone who is (or potentially will be) materially affected by the outcome of the project.

Business analyst is a high-level role responsible for the business analysis and BPM work activities. The business analyst performs process modelling and creates functional specifications for process. This high-level role implies that the analyst might also take on more specialized tasks during the project.

Architect is a high-level role responsible for the overall work effort of creating the architecture and design of the system. More specialized roles, such as enterprise architect, application architect, SOA architect, and infrastructure architect, are actually responsible for various architectural work efforts that constitute the design phase of the project.

Developer is a high-level role responsible for the implementation of the solution (services).

Tester is a role responsible for performing activities required to test the application before it is deployed into a production environment.

Line Manager is a person who heads revenue generating departments (manufacturing and selling) and is responsible for achieving an the organization's main objectives by executing functions such as policy making, target setting, decision making.

Work Products

This section describes the outputs generated by the methodology. Thus outputs are quite a bit through the methodology phases and therefore only main outcomes of the various phases are described.

Business Requirements are requirements based on customer's wishes and their needs. They describe the principles and functioning of a company as a whole, defining its objectives [START_REF] Shuja | IBM Rational Unified Process Reference and Certification Guide: Solution Designer[END_REF]. Identified processes are part of these requirements.

System Requirements are the modeling phase results. These are the requirements for creating an information system built on business processes. Detailed and hierarchically organized process diagrams are part of this output. These requirements also describe the level of process automation.

Information System is a result of the configuration phase. Modeled processes are configured and composed of individual services. Thus created processes are deployed on a process engine, which interprets them. The workflow engine also allows interaction with users and external tools.

Monitoring data is an output of the monitoring phase. It contains information about process instances run, such as duration or cost of individual processes. It also records the passage through the business process, which can be useful in further analysis.

Strategic plans are the optimization phase outputs. The overall technology improvement (TQM, BPR) and strategic plans for further business process improvement are chosen in strategic plans.

In case the methodology is used in the field of emergency management it is appropriate to consider certain features that arise from this specific area. This includes clearly defined organizational structure, legislative and other issues. Some features are illustrated on emergency management in the Czech Republic.

Organizational Structure

The Integrated Rescue System (IRS) is determined for co-ordination of rescue and clean-up operations in case, where a situation requires operation of forces and means of several bodies, e.g. fire fighters, police, medical rescue service and other bodies, or in case, where the rescue and clean-up operation is necessary to be co-ordinated from the Ministry of Interior or by a leader of region's level, or by mayors of municipalities with extended responsibilities [START_REF] Ludík | Process Support for Emergency Management[END_REF]. As the Integrated Rescue System are therefore considered the co-ordinated proceedings of its bodies during preparations for emergencies, and during rescue and clean-up operations.

As permanent authorities for coordination of Integrated Rescue System bodies are considered the operational and information centres of the Integrated Rescue System, i.e. the operational centres of regional Fire Rescue Services and the Operational and Information Centre of the General Directorate of the Czech Fire Rescue Service.

Legislation and Documentation

There are many laws dealing with emergency management in the Czech Republic. Crisis management elements are codified in the Law No. 240/2000 on crisis management and on modification of certain codes (Crisis Code), in latter wording. Based on this law, state of danger can be proclaimed to overcome unfavourable trends of development. The other important Law is the Law No. 239/2000 on the Integrated Rescue System and on amendment of certain codes, in latter wording, is the basic legal frame describing situation around IRS.

Another feature of the emergency management is the detailed documentation that defines how to proceed in particular situations. The Contingency Plans belong to the basic documents. They contain a set of measures and procedures addressed to crisis situations, i.e. they contain sum of planning, methodological and informational documents used in decision-making, management and coordination of tasks in the emergencies.

Different Types of Information

To deal successfully with critical situations, it is inevitable for all IRS components to have all the necessary information at their disposal. It is often not trivial because the information used in emergency management can have three basic characteristics or dimensions: time, space and aggregation. The time dimension of the data is important in the crisis situation with dynamic character. It could be contamination spreading or the direction of wind during the fire fighting. This information varies with time so it is a relevant factor in dealing with the crisis situation. Another important aspect of the information is that it is bound to the intervention place. It is only the limited area around the intervention place that is important and it can be defined according to the character of the crisis. The last dimension of information relevant to dealing with a crisis situation is aggregation. The data is provided to the intervention units in aggregated form, for example as specific maps or map layers. However, they contain also specific data sets that could be irrelevant to the character of a particular intervention location or to the crisis itself. The way to avoid unnecessary information is to use adaptive mapping [START_REF] Kubíček | Process Support and Adaptive Geovisualisation in Emergency Management[END_REF].

Psychological Aspects

There is a new belief that even despite the devastating impact of disasters, substantial lack of resources, and general chaos, there is still a possibility of carrying out some actions that will serve in maintaining at least the basic integrity of the human society and its dignity. Psychological aspects are usually very important for dealing with crises. All activities of crisis management are performed under substantial time and psychological pressure. Intervention commanders work and make decisions in fear of their possible failure. They often have insufficient and inaccurate information at their own disposal during chaotic development of the situation. Other problems arise from lack of necessary resources, like working tools and necessary equipment or integrated rescue system resources. The basic requirements of life may sometimes be restricted under the influence of all these factors.

Using of Standards

Unified Modelling Language (UML) is a standardized modelling language used in the field of software engineering. Two diagrams are suitable for process modelling. One of them is Use Case Diagram, which shows the functionality provided by the system in terms of actors, their goals represented as use cases. Then it depicts all the relations among those use cases. The other is Activity Diagram. It represents the business and operational step-by-step workflows of components in a system.

Business Process Modelling Notation (BPMN) provides a notation that is understandable by all business users, from the business analysts that create the initial drafts of the processes, to the technical developers responsible for implementing the technology that will perform those processes, and finally also the business people who will manage and monitor those processes. This way, BPMN creates a standardized bridge over the gap between the business process design and process implementation.

Web Services Business Process Execution Language (WS-BPEL) defines a model and a grammar for describing the behaviour of a business process based on interactions between the process and its partners. The WS-BPEL process defines how multiple service interactions with these partners are coordinated to achieve a business goal, as well as the state and the logic necessary for this coordination.

The primary contribution of this paper is the innovative, process-oriented methodology. The methodology is described in terms of phases, user roles and work products. The paper also describes the set of recommendations, which should be applied when methodology is used on emergency management processes. Recommendations are based on practical experiences with solving of the research plan called Dynamic Geovisualisation in Crises Management [3] [4]. The research plan also illustrates the practical use of the methodology in real situations, for example in accidents with dangerous substances.

It is appropriate to emphasis on adequate software support during the methodology use. This support is provided by Business Process Management Suite (BPMS), where different tools support different methodology phases. In case of a comprehensive emergency management system it is necessary to take the close interoperability to GIS or other operational systems used by the IRS into account. Therefore it is recommended to add the global architecture which will illustrate the overall deployment of the system based on business processes.

The subsequent objective of this research is to define in detail the methodology phases in terms of individual tasks and their links to each other. This is related to the assignment of responsibilities for these tasks. Similarly, detailed description of the role associated with implementing the methodology in terms of ICT and EM is needed. The final aim is to describe in detail the tasks inputs and outputs in terms of work products and determine whether all information is available at the right time.

Acknowledgments. The contribution is a part of the research plan no. MSM0021622418 and the research project no. FRVS/1035/2011, both supported by the Czech Ministry of Education, Youth and Sports.


Jaromír Adamuška
email: jaromir.adamuska@mzp.cz

Simona Losmanová
email: simona.losmanova@cenia.cz

The National Secretariat GEOSS/GMES -Implementation of GEOSS and GMES in the Czech Republic

Keywords: GMES, GEOSS, GEO, European Commission, National Secretariat, environment, data, users, services, coordination, remote sensing

The aim of the article is to present the role and activities of the National Secretariat GEOSS/GMES (NS). Existence of the NS was declared by the Czech Governmental Resolution No. 229/2009. This resolution requires activities of two ministries -the Ministry of the Environment and the Ministry of Education, Youth and Sports to coordinate Czech activities related to international programmes GEOSS and GMES. The goal of GEOSS and GMES is building up an integrated and sustainable system by sharing and providing information services on the environment, especially those based on Earth observation and remote sensing technologies. The article explains the Czech approach to the coordination of GEOSS/GMES. The coordination is necessary for efficient exploitation of the first-emerged services and also for expanding information to the user´s community. The NS Work Plan 2011 was introduced at the beginning of this year and some goals have already been achieved.

Introduction

The Czech Republic is actively participating in the European programme Global Monitoring for Environment and Security (GMES) by expanding awareness about GMES at the national level through the implementation of developing knowledge and best practices into a governmental management system in the Czech Republic. GMES is often presented as the second pillar of the EU space policy after the Galileo programme. However, GMES is not only related to space. The GMES programme consists of three elements: the space component, the in-situ component and services driven by the user´s needs.

There are many definitions of GMES. All of them reflect the original idea from the Baveno Manifesto in 1998. Some parts of GMES can be emphasized by different interest groups. Maybe the most accurate description of GMES is the one which says that GMES is a framework for sustainable, standardized and operational services used for efficient management of environmental, ecological and social threats. Moreover, different approaches to GMES increase the importance of this programme as the EU contribution to the worldwide GEOSS (Global Observation System of Systems) initiative developed by The Group on Earth Observation (GEO).

After more than 12 years of continuous effort, GMES finally advanced to the preoperational phase. The most important milestone was the issuing of Regulation No. 911/2010 [1], which entered into force on 9 November 2010. This regulation shifted GMES from an initiative to a programme or, in other words, to the stage when GMES gained its own budget, and also enforced its relevance and importance. Not surprisingly, an increase of claims and a growth of the agenda concerning the coordination between the EU and Member States is anticipated. The key aspect is a usage of the maximum potential that this programme brings. Each Member state has a different approach how to tackle this issue. This article describes the Czech approach to the coordination of GMES activities within the Czech Republic.

Czech approach to GMES

The Czech Republic has been increasing its involvement in the European programme Global Monitoring for Environment and Security (GMES) by different actions since November 2010 in order to be very well-prepared for the year 2014 when GMES should be fully operational. The first step of the Czech Republic's involvement within the GMES initiative emerged at the end of 2006. At that time, the Czech government approved the engagement of the Czech Republic with GEO and agreed to the building up of the integrated and sustained GEOSS, as well as agreed with its participation in GMES. The competence for the coordination and carrying out of these issues has been given to two ministries: the Ministry of the Environment (MoE) and the Ministry of Education, Youth and Sports (MoEYS). The MoE includes those connected with the environment -major users or potential data producers (or service providers). The MoEYS represents the research sector -institutions which are able to participate in the developing and the designing of services and associated technologies. In 2006, GMES was in its early stage and had the status of an initiative. Particular projects were exclusively funded from the EU Framework programme (FP6 and later from FP7). The first aim of those projects was to develop pilot services based on existing data sources to prove potential and possibilities, need and usage of GMES. After finishing these projects, it was necessary to maintain the vital built-up services (Framework Programmes for R&D do not intend to operate project outputs). To cope with this issue was crucial and it was due to one of these mentioned cases which led to the preparation of the new legislation to underpin GMES.

This context was also reflected at the national level. The priority was to set up an organizational framework for the coordination of GMES activities within the Czech Republic and to ensure the consultation of prepared EU legislation between 2007 and 2010. It proceeded through both ministries which were involved at the national level as well as through Czech delegates from the GMES Advisory Council of the EU. In relation to the increasing need for better GMES coordination, the Czech government authorized the MoE and the MoEYS in spring 2009 to establish the National Secretariat of GEOSS/GMES (NS) as an operational body to deal with GMES and GEOSS issues. This particularly gave power to the NS to regulate and advise public institutions for the purchasing of remote sensing data. The NS was established in July The first completed goal of the NS was to launch the Czech portal (www.gmes.cz) at the end of 2010. This portal provides comprehensive information about GMES and GEO activities in a bilingual version -Czech and English. Visitors may find here useful information about the evolution of GMES and GEOSS along with the structure scheme of the NS. Services like downloading documents, signing for newsletters or asking questions are offered via the e-mail address: info@gmes.cz. A calendar with interesting events such as project calls, conferences, seminars and workshops is updated regularly together with news section [START_REF]GEOSS/GMES in the Czech Republic[END_REF]. Another step was the completion of the document called "Strategy of the Implementation of GEOSS and GMES in the Czech Republic 2011-2013". This internal document represents a road map type plan and summarises the aims and priorities of the activities for the NS members. At the same time, the strategy presents the main principles and spheres of the NS work and specifies its particular activities.

Members of the NS represent the Czech Republic within the EU and sit within different bodies. One of them is the recently established User Forum managed by the GMES Bureau which is driven by the EU General Directorate for Enterprise and Industry (DG ENTR). The second one is the GMES Committee (GC). In fact, the Czech Republic is represented at the GC´s meetings by two delegates independently nominated by the MoE instead of the NS´s members. In spite of this, they closely and independently cooperate with the NS (consultation of voting instructions, etc.). The NS can also cover delegate's participation if the need arises. Currently the NS is helping to find the best suitable candidate for the Security Board.

There is also an active cooperation with the Ministry of Transport and the Ministry of Interior as an example of interaction on the highest institutional level. The communication with other relevant public authorities is secured by the Coordination Board of GEOSS/GMES (CB). The CB, consisting of various public representatives, shall collect GEOSS/GMES positions and needs of other resorts. The CB recommendations help the Ministry of the Environment to specify main NS´s forehead activities.

Conclusion

Considering the tight time frame, the National Secretariat of GEOSS/GMES (NS) has achieved a lot. The necessity for the coordination procedures connected with remote sensing data is undisputable. The NS was established not only as the advisory body and as the coordinator, but was also created for saving national expenditures. Therefore, the NS will be the main contact point for relevant public institutions to discuss their needs. The NS will provide them with correct information about available remote sensing data and opportunities to engage themselves with GMES projects. It is very important to highlight the fact that remote sensing data coming from the EU to Member States should be free for public authorities. The NS will provide information about GMES and GEOSS through different types of media (internet website, oral presentations, leaflets). The NS will motivate experts from research or academic institutions and also support SMEs to benefit from GMES. The NS will also ensure consultation of the EUs strategic and legislative materials.

Overall, the NS will help the Czech Republic to eliminate or to reduce information gaps by close cooperation among institutions.

Future advantages will depend on the quality of cooperation among all parties involved.

  2010 and has members from the most re from the MoE who is responsible for official communication within national authorities Environmental Information Agency; focus overlaps to INSPIRE and SEIS. The second specialist Office and he specialises in through the EU Framework a representative atmosphere, hydrology, climate and meteorology.

Fig. 1 .

 1 Fig. 1. The Czech portal


Ladislav Dušek

Jiří Hřebíček

Miroslav Kubásek

Jiří Jarkovský

Jiří Kalina
email: kalina@iba.muni.cz

Roman Baroš

Zdeňka Bednářová

Jana Klánová

Ivan Holoubek
email: holoubek@recetox.muni.cz

Conceptual Model Enhancing Accessibility of Data from Cancer-Related Environmental Risk Assessment Studies

Keywords: Persistent organic pollutants, cancer risk, data model, data discovery

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

1. retrospective exploitation of data sources and their description in discovery process 2. prospective arrangement enabling effective electronic data capture in future From the viewpoint of informatics, environmental risk assessment can be characterized as processing of heterogeneous data leading to probabilistic estimation of some uncertain (prospective approach) or on the other hand relatively certain (retrospective approach) risk event. Main complications that hamper progress in this field are highlighted in the following list:

1. Extremely wide range of data types and structures in environmental studies 2. Insufficient metadata description and standardization 3. Lack of well established repositories based on standardized protocols which is in strong contrast to methodical progress in environmental and medical sciences 4. Variability of technologies, coding and reporting systems used by different research groups 5. Growing number of small and not adequately published and described studies, which however produce valuable and important data. Especially last point deserves special attention. Growing number of studies is not accompanied with adequate progress in information technologies and in practical implementations of SW tools [START_REF] Elmagarmid | Management of Heterogeneous and Autonomous Database Systems[END_REF][START_REF] Grossman | Information Retrieval: Algorithms and Heuristics[END_REF]. It inevitably results in publishing of nonconsistent outcomes with ad hoc data management support. To discover such broadly heterogeneous data we need consensus on data and metadata standardization, but it itself is not enough. We need sufficiently complex conceptual models, advancing development of formal ontologies over environmental and epidemiological data capture systems.

Although there are some usable standardizing concepts already published (Ecological Metadata Language) 1 [START_REF]Darwin Core: Darwin Core Schema (version 1.3), a draft standard of the Taxonomic Database Working Group (TDWG)[END_REF][START_REF]DCMI Metadata Terms[END_REF], they are not extensively used in practice or there is a lack in support in data capture systems. Environmental data collection is still subjected to research in the informatics field [START_REF] Athanasiadis | A methodology for developing environmental information systems with software agents[END_REF][START_REF] Huang | Effective environmental management through environmental knowledge management[END_REF]. In cancer research, we can take the advantage of accessible nomenclature standards like Clinical Terms (SNOMED-CT) 2 , the Unified Medical Language System (UMLS) 3 , and the National Cancer Institute Thesaurus (NCIT) 4 . Several consistent attempts to design ontologies functional for cancer treatment and management have also been published recently [START_REF] Brochhausen | The ACGT Master Ontology and its applications -Towards an ontology-driven cancer research and management system[END_REF].

Nevertheless, widespread support for ontology-based approaches is not implemented in the field ecological risk assessment [START_REF] Madin | An ontology for describing and synthesizing ecological observation data[END_REF]. However, interest in developing ontologies is growing, because new synthetic environmental analyses increasingly rely on access to a broad range of cross-disciplinary data sources and monitoring studies. The effective system should encompass not only structure and content of such data repositories, but also hierarchical architecture and mutual relationships among components [START_REF] Williams | Ontologies for ecoinformatics[END_REF].

That is why we try to propose multidisciplinary conceptual model with ambition to discover and process data on environmental pollution and cancer risk using the same methodical template. Our principal aim is to support data integration across the geographical borders, disciplines and professional terminologies; as well as integration of newly gathered data with data already collected and archived.

Persistent organic pollutants as model for cancer risk studies

Nowadays, many environmental factors, both chemical and physical, are proved to be involved in causing cancer. In our proposal of conceptual data model for cancer risk studies, we took persistent organic pollutants (POPs) as a proper model namely due to the following reasons:

-POPs have become recently intensively studied due to their properties which represent remarkable risk for ecosystems and human population (persistence, bioaccumulation, carcinogenicity, genotoxicity). The ability to accumulate in human tissues together with persistence enable long term exposure of an individual, the effect of which is further enhanced by genotoxic impact. -Clear nomenclature of POPs which facilitates development of formal concepts based on well defined entities. -There are important international initiatives to control distribution and associated impacts of POPs; participating countries and institutions form growing family of potential users of developed informatics solutions. The decisions by the Conferences of the Parties to the Basel, Rotterdam and Stockholm conventions on enhancing cooperation and coordination among the three conventions invite Parties to coordinate their efforts when implementing the three conventions to ensure close cooperation among relevant ministries and programmes at the national levels [START_REF]UNEP Report of the First Expert Meeting to update the Guidance on the Global Monitoring Plan for Persistent Organic Pollutants[END_REF]. Not all POPs are proved as direct carcinogens or co-carcinogens. There are several widely accepted systems reporting data on carcinogenicity (IARC: International Agency for Research on Cancer; US EPA: United States Environmental Protection Agency; ACGIH: American Conference of Governmental Industrial Hygienists, see also Table 1). Therefore, this property is an important classifier extending information content given by nomenclature itself.

Cancer epidemiology as endpoint in risk studies

Growing cancer burden affects visible proportion of worldwide population and logically attracts research interest. Furthermore, the era of personalized medicine put the cancer diseases to the position of primary target of IT support [START_REF] Sotiriou | Taking gene-expression profiling to the clinic: when will molecular signatures become relevant to patient care?[END_REF][START_REF] Tsiknakis | A semantic grid infrastructure enabling integrated access and analysis of multilevel biomedical data in support of postgenomic clinical trials on Cancer[END_REF]. Studies focused on cancer epidemiology address the virtual top of the system of cancer research. Cancer is however entirely multifactorial problem with its roots in molecular mechanisms inside the cells. Therefore, the main added value of IT is to facilitate integration of data acquired from multiple levels of investigation. The knowledge of mechanisms explaining cancer origin can mostly explain changes observed in the population level, or at least minimize the bias in interpretation.

Cancer-related environmental risk studies require very comprehensive data background. We typically evaluate probability of increased risk for a given population, typically exposed to some dangerous factor. Prospective studies work namely with laboratory data and estimate intensity of probable exposure pathways. Retrospective studies utilize accessible epidemiologic observations performed in a target area. Estimates of incidence, mortality or prevalence are employed as frequent en-points.

Most of the investigations studying harmful effect of chemicals on population cancer burden are designed as case control studies, which often determine their frequently inconclusive results. We can mention problems with mutual variations in outcomes of similar studies, insufficient power of accessible retrospective records on individual environmental exposures, lack of biomarkers reflecting stages of the carcinogenic process or problems with sufficiently long time series of observation [START_REF] Sram | Future research directions to characterize environmental mutagens in highly polluted area[END_REF][START_REF] Kyrtopoulos | Biomarkers of genotoxicity of urban air pollution. Overview and descriptive data from a molecular epidemiology study on populations exposed to moderate-to-low levels of polycyclic aromatic hydrocarbons: the AULIS project[END_REF]. Origin of analyzed data, precision and validity of measurements are therefore very important attributes to be followed in these studies.

Proposed conceptual model and reasoning of its structure

Here we proposed simplified conceptual model that should broaden our capability for understanding the validity, content and relevance of the data coming from environmental and epidemiologic monitoring (Table 1). The adopted concept should support scientists in mapping of cancer-risk. The proposed model is based on hierarchically layered architecture providing different levels of classifiers or properties of homologous entities as well as scoring of data origin. The model works with three principle layers:

1. Entities (POPs or cancer diagnoses) defined on the basis of internationally standardized nomenclature systems. The level is linked with classifiers, i.e. given properties extending the nomenclature and filtering homologous groups of entities. 2. Observation -measurement level and its descriptors, focused namely on time & space coordinates, methodical attributes, measured endpoints, reference benchmarking of their value and validity scoring. 3. Content identification describing employed measures, units and precision estimates. Important principles applied in conceptual model construction are further summarized here:

1. Reduction of the number of object properties is important and practical; when necessary, the set of attributes can be expanded before specifically designed data discovery. Too many object properties cannot be utilized efficiently in retrospective exploitation of the resources. 2. Any relevant data discovery must reflect heterogeneity of experimental and methodical approaches at ecosystem and population level. That is why type of the study or data resource is obligatory attribute among the observation descriptors.

3. A measured value cannot be interpreted without reference to a defined/known measurement standard or reference benchmarks. Both internal reference norms (e.g. self-benchmarking of time series data) and external benchmarks (e.g. background concentration levels or limits, hygienic norms, detection limit of applied method, international epidemiologic burden) are used. 4. Descriptors of measures must fulfill obligatory Measurement Standard, i.e.

the units, scales and lists of attributes defining origin of the measures (e.g. examined matrix, sampling methods, investigated population, cohort, demographic selection etc.) We proposed the same discovery template for POPs and cancer resources as it is summarized in Table 1. From the first step, we must categorize the key subjects, i.e. POPs and cancer diagnoses. At this obligatory tier, internationally validated nomenclature is recommended and summarized in Tables 2 and3. The subsequent tiers gradually unravel attributes important for interpretation of cancer risk studies (e.g. carcinogenicity of POPs, malignant/benign classification of cancers, data origin in terms of study design, etc.). These layers form multidimensional descriptive space which is significantly more robust than any single formal classification. This minimizes the probability of missing or omitting of some important facts and protects the solution against selection bias or misinterpretation.

The same template used for POPs and cancer risk enables the IT tools to integrate these data resources. The relevance of the integration process relies on the ability to determine if two values (studies) are compatible, not only in time and space coordinates. Description of model levels in table 1 implies interdisciplinary interactions of classifiers extending the nomenclature (e.g. "carcinogenic compound x malignant neoplasm"), observation -measurement validity identifiers (e.g. long-term national monitoring of POPs x national cancer registry) and mutually related observation-measurement (e.g. trend in POPs concentration in food chains vs. disease specific mortality due to GIT cancers). The most important added value of the model is the capability to determine if two data sets can be either fully or partially merged or mutually related once they are discovered. To decide it, the system undertakes important steps in all levels of proposed architecture:

1. The system must control relevance and compatibility at the taxonomic level (nomenclature) and in space & time localization of data resources 2. Identified data resources must be assessed if, and at what semantic resolution, the data are compatible (level of classifiers and/or extending descriptors like type of the study, etc.) 3. Finally, the measurement standards for the mutually related environmental and epidemiologic endpoints have to be controlled for compatibility (units, scales, reference benchmarking, methodical origin). Obviously, not all descriptors must be necessarily fulfilled in all data discovery sessions, sometimes the uncertainty is too high. Different situations give to different tiers different weights. For example, the situation is thoroughly different if someone needs well designed retrospective or prospective assessment than if it is sudden catastrophic situation like exposure due to industrial accident where we must in first line mitigate the immediate effects. Moreover environmental factors cannot necessarily impact upon human population is some isolated system, highly probably they interact with other harmful effects associated with life style, occupational factors and relating exposures causing probable carcinogenic synergies. It complicates the interpretation of population risk studies where precise and standardized description of input data is becoming a key step limiting the relevance of reached outcome.

Projection of proposed conceptual model to data standards

The quality of conceptual model determines its utility for assisting in data discovery and information searching. However, the applicability of any such model strongly depends on quality of description and content of processed data sets. That is why, we should insist on minimized, but obligatory database components, i.e. limited number of entities and their descriptors. Minimized data model as standard can be used both retrospectively (scoring of validity and usefulness of discovered resources) and prospectively (when designing new data capture systems). Proposed conceptual model intrinsically encompasses these obligatory items:

1. POPs data resources -institution (origin of data), time & space coordinates, type of resource (study design), examined entities (compounds), measures and methodical descriptors (experimental units, values & units, matrices, methods) 2. Cancer risk data resources -institution (origin of data), time & space coordinates, type of resource (study design, examined effects), examined entities (tumors, cancer diseases), measures and methodical descriptors (experimental units, values & units, cohorts, methods) The system allows any type of reasonable extension; additional properties may be added on demand. However, minimized data standard ensures accessibility of key information namely in Measurement level of the model; i.e. when and where measurements were recorded, who recorded each measurement, the methodology of measurements, study design and aim. In this way the model can improve data visibility to search engines and enables greater levels of automation of common data transformation, summarization and integration.

Proposed conceptual model also contributes to widely recommended discovery of data based on the concepts they really represent [START_REF] Madin | An ontology for describing and synthesizing ecological observation data[END_REF][START_REF] Berkley | Metacat: a schema-independent XML database system[END_REF][START_REF] Borgida | Description logics in data management[END_REF]. In contrast to formal frameworks usually published with focus on one discipline [START_REF] Williams | Ontologies for ecoinformatics[END_REF][START_REF] Bard | Ontologies in biology: design, applications and future challenges[END_REF] our model presumes search which exploits relationships between classes within environmental and human data sets as well as interdisciplinary relationships between the two areas. The concept supports development and formalization of ontologies, relevant for both environmental sciences and cancer epidemiology.

Ontology should represent the knowledge in a domain of interest, defined via the terminology (concepts, nomenclature) used within the domain and the properties and relationships among domain objects [START_REF] Baader | The Description Logic Handbook: Theory, Implementation, and Applications[END_REF]. This concept is fully implemented in the model proposed here; the nomenclature baseline is extended by selected descriptors with defined dependencies. It is a formal framework for observational studies where we adopted structured approach recognizing key entities (nomenclature classes) in the 1 st level and their characteristics (classifiers) important for the cancer risk studies.

Second level consists of measurements and their characteristics, i.e. validity criteria, origin of data, etc.. Third level covers content identification, namely values and units, scales.

Impact of proposed data model on data processing and analysis

Population studies focused on cancer risk are complex and require processing of highly diverse data. Even if we can get adequate data sources accessible for analyses, it is often difficult to select the best approach how to mutually relate measured factors; mostly our later analytic steps assume some specific input or data aggregation from the preceding measurements. That is why the data structure must be well defined but at the same time, flexible enough to reflect a wide range of possible hypotheses. Regarding heterogeneity of environmental problems, no unique, definitely the best model can be recommended. Of course, such system cannot be constructed retrospectively, on demand of running analyses. Baseline standardization proposed here in conceptual data model positively impacts upon analytical procedures, namely in the following three fields.

 Hierarchical structure advances the data analysis. The proposed conceptual model intrinsically distinguishes hierarchy of levels and descriptors which facilitates implementation of tools focused on data analysis and knowledge mining. The position of nomenclature entities and measurements can be used to denote a wide range of entity characteristics (nominal or ordinal measures of existence, prevalence). Using the hierarchy of descriptors we can easily decide whether the data are useful for a particular analysis.

 Conceptual model supports robust reference comparison of values.

Regarding data analysis, very important attribute of the proposed model is incorporation of measurement level and its characteristics. Validity criteria reflect some precision measures as well as reference values or protocol standards. A measured value cannot be interpreted and analyzed without reference to a defined measurement standard.  Stratified analyses and integration of different data sources. Hierarchical relationships among nomenclature classes and descriptors also potentiate development of automated SW tools for comparison of values using different strata. For example we can summarize prevalence of some cancer according to site locations because the sites and their population provide context for observation of cancer load. Similarly, the sites and matrices provide a context for measurement of POPs exposure. Both summaries can be then interlinked using various time frames. The concept thus facilitates evidencebased data integration, reasoned by compatibility of interlinked values.

Proposed model has already been used and implemented in SW toolkit focused on data discovery over Czech National Cancer Registry (system SVOD5 , [START_REF] Dušek | Epidemiology of malignant tumours in the Czech Republic[END_REF]) and on processing of data from various POPs monitoring networks (system GENASIS 6 ). Both information systems distinguish object entities (nomenclature items) and enable users to stratify accessible measures (content of resources) across a set o classifiers and methodically important attributes.

Conclusions and future challenges

In this study we proposed interdisciplinary conceptual model as a support of cancerrelated environmental risk studies. The model can be useful in basic characterization of data standards and context of observations, as well as for information search. The model intrinsically defines dependency of obligatory descriptors and hierarchy in nomenclature attributes and supports establishment of data repositories with respect to other meaningful dimensions like cancer-related properties of chemical compounds, origin of data, coding of extreme or unusual values, etc. Such repositories allow the scientists to work with functional properties of nomenclature entities and related measurements. Moreover, measurements are linked to internal and external reference benchmarks which subsequently facilitate data integration or summaries. Of course, many barriers that limit interdisciplinary data discovery still remain. The problems refer intrinsically to the information reachable in observation studies and cannot be easily solved by informatics. In cancer risk assessment, it is hard to collect representative data in relatively short period of time. Timescales here are long and the ability to switch a system to more complex level is limited by cost and organizational constraints. It is mostly not possible to carry out adequate assessment of the large scale systems with techniques that have been successful in smaller systems with limited heterogeneity.

Therefore, completely new methodical and experimental approaches are needed, especially those introducing novel, more sensitive and specific indicators. Population monitoring using methods of molecular epidemiology combined with reliable data on exposure offers such new powerful approach to determine the effect of genotoxic agents on human populations [START_REF] Kyrtopoulos | Biomarkers of genotoxicity of urban air pollution. Overview and descriptive data from a molecular epidemiology study on populations exposed to moderate-to-low levels of polycyclic aromatic hydrocarbons: the AULIS project[END_REF]. Study on the genetic polymorphism that can be a risk indicator for cancer development is a newly occurring stream in environmental sciences [START_REF] Knudsen | Risk assessment: the importance of genetic polymorphisms in man[END_REF]. This methodical progress is making possible the collection and organization of biological informatics at an unprecedented level of detail and in extremely large quantities.

No. 247893 TaToo -Tagging Tool based on a Semantic Discovery Framework) granted by European Commission.

Appendix

RESOURCE IDENTIFICATORS

Obligatory descriptors identifying institution (project) which guarantees the data (mostly also as owner of the resource content).

In already closed resources, the identification is supplied with overall time/ space description.

OBJECTS -KEY ENTITIES

NOMENCLATURE

Internationally used nomenclature of POP compounds (UNEP, 2010) -see Table 2. System allows selection of individual compounds and their groups.

OBJECT CLASSIFIERS

Categorized classifiers derived from external (encyclopedic) sources of information. Classifiers define groups but can be used also for scoring of individuals. Classifying criterion is linked to the individual compounds and/or to their groups.

Carcinogenicity

Attribute extending recognition of nomenclature classes, coded as no/yes/suspected. Code is directly interlinked with individual compounds. There are several international database sources of this information (IARC/US EPA/ACGIH) -see table 1.

Reference concentration values

Internal (time series analysis, background values) and/or external reference benchmarks. The classifier is coupled with given entity (compound), typically with direct link to matrix sampled and method used.

OBSERVATION -MEASUREMENT (OM) TIME & SPACE COORDINATES

Obligatory attributes, also proposed as inescapable items of any data standard.

CONCEPTUAL MODEL -LEVELS DEFINITION & COMMENT

RESOURCE IDENTIFICATORS

Obligatory descriptors identifying institution (project) which guarantees the data (mostly also as owner of the resource content). In already closed resources, the identification is supplied with overall time/ space /population description.

OBJECTS -KEY ENTITIES

NOMENCLATURE

Internationally guaranteed system of classification of diseasessee Table 3. "Cancer" is used in many synonymous terms: tumor, neoplasm, metastasis.

OBJECT CLASSIFIERS

Categorized classifiers derived from external (encyclopedic) sources of information. Classifying criterion is linked to the individual cancer diagnosis and extent its information value Nomenclature subsystems (TNM classification)

Internationally standardized nomenclature of cancer diseases, based on ICD-O-3 as key system and ICD-10 as multi-component subsystem for identification of malignant neoplasms.

Tumor type

Classifier important for risk studies focused on some type of harmful exposure (list): malignant / benign / unknown behavior

Reference values

Internal (time series analysis, background values) and/or external reference benchmarks (internationally reported values; reference epidemiological characteristics). The classifier is coupled with given entity (cancer type) and epidemiological measure, typically with relation to type of population observed.

OBSERVATION -MEASUREMENT (OM) TIME & SPACE

Obligatory attributes, also proposed as inescapable items of any

Table 1 .

 1 Conceptual model proposed for environmental cancer-related data management 1a. Resources of persistent organic pollutants (POPs)

	CONCEPTUAL
	MODEL	DEFINITION & COMMENT
	-LEVELS

Table 2 .

 2 List of POPs from annexes A, B and C of the Stockholm convention and their congeners according to recommendation for monitoring from the first workshop that considered the 2nd revision of the Guidance document for the GMP, held 12-14 April 2010 in Geneva (UNEP 2010) . Carcinogenity groups IARC/US EPA/ACGIH, -means, that the substance is not listed. Toxned database was used: http://toxnet.nlm.nih.gov/cgi-bin/sis/search. IARC: International Agency for Research on Cancer (http://monographs.iarc.fr/index.php) United States Environmental Protection Agency (http://www.epa.gov/iris/)

	COORDINATES	data standard.
	STUDY TYPE	Study type (list): National epidemiological registry / Local
	(design)	(regional) registry / Hospital-based project / Cancer screening /
	PROBLEM STUDIED	Clinical trial / Cohort study / Case-control study / Descriptive
	(exposure)	epidemiologic observation
		Problem studied (list): Genetic factors, hereditary syndromes /
		Life style factors / Demography, ageing, gender studies /
		Occupational factors / Environmental factors / toxic exposures
	METHODICAL	Obligatory identification of observation -measurement,
	ATTRIBUTES	necessary for interpretation of measured values. Measured entities
	Measured entities	select nomenclature items which are examined in given
	Matrix	population. Experimental unit identifies context of measured
	Experimental unit	values	(representative	population,	selected	cohort).
	Sampling/measurement	Sampling/measurement methods fulfill minimized list of items
	methods	which follows standardized guidelines for epidemiologic
		observation studies.
	CONTENT
	Measures	Content of the resource, typically recognized epidemiological
	Units	measure (incidence, mortality, prevalence) in internationally
	Precision measures	standardized unit scales (crude estimate, ASR, etc.). Precision
		measures include population representativeness (coverage) of the
		data resource.

1

http://knb.ecoinformatics.org/software/eml/

http://www.nlm.nih.gov/research/umls/Snomed/snomed_main.html

http://www.nlm.nih.gov/research/umls/

http://www.mindswap.org/2003/CancerOntology/

http://www.svod.cz

http://www.genasis.cz

Acknowledgements. This research received financial support from the CETOCOEN project of the European Structural Funds (CZ.1.05/2.1.00/01.0001) and project FP7

International Classification of Diseases, 10 th edition (ICD-10) (C00-C14) Malignant neoplasms, lip, oral cavity and pharynx (C15-C26) Malignant neoplasms, digestive organs (C30-C39) Malignant neoplasms, respiratory system and intrathoracic organs (C40-C41) Malignant neoplasms, bone and articular cartilage (C43-C44) Malignant neoplasms, skin (C45-C49) Malignant neoplasms, connective and soft tissue Malignant neoplasms, breast and female genital organs (C60-C63) Malignant neoplasms, male genital organs (C64-C68) Malignant neoplasms, urinary organs (C69-C72) Malignant neoplasms, eye, brain and central nervous system (C73-C75) Malignant neoplasms, endocrine glands and related structures (C76-C80) Malignant neoplasms, secondary and ill-defined (C81-C96) Malignant neoplasms, stated or presumed to be primary, of lymphoid, haematopoietic and related tissue (C97) Malignant neoplasms of independent (primary) multiple sites (D00-D09) In situ neoplasms (D10-D36) Benign neoplasms (D37-D48) Neoplasms of uncertain or unknown behavior International Classification of Diseases for Oncology, 3rd Edition (ICD-O-3) -overview Topography. The topography of the tumor is described by topographical code. Topographical code corresponds to the C section of ICD-10 (exceptions are listed in ICD-O-3).

Morphology. The morphology provides five-digit codes ranging from M-8000/0 to M-9989/3. The first four digits indicate the specific histological term. The fifth digit after the slash (/) is the code, which indicates whether a tumor is malignant, benign, in situ, or uncertain.

Grade. A separate one-digit code is also provided for histological grading (differentiation


P Kutschera
email: peter.kutschera@ait.ac.at

M Bartha
email: mihai.bartha@ait.ac.at

D Havlik
email: denis.havlik@ait.ac.at

SUDPLAN's experiences with the OGC-based model web services for the Climate Change usage area

Keywords: Environmental modelling, Open Geospatial Consortium, Sensor Web enablement, OGC SWE, Model Web, Observation Web, Time Series Toolbox, Sensor Service Architecture, SensorSA

SUDPLAN is currently developing a technical solution for Model Web/Observation Web in the Climate Change usage area. Proposed solution is based on the Open Geospatial Consortium standards, and follows the ideas expressed in SANY Sensor Service Architecture (SensorSA). SUDPLAN also continued the development of the SANY software, resulting in native SOS, SPS and 2D coverage support in "Time Series Toolbox" framework for building sensor web applications. SUD-PLAN re-uses much of the OGC SWE and SANY SensorSA functionality to: (1) configure and run the models; (2) provide the data (observations) required for model execution; (3) inform the user on model run progress and (4) access the model results. In this paper, we shall describe the SUDPLAN's experiences with implementing of the interoperable Model Web using OGC standards, and discuss the advantages of various services from the OGC SWE suite as compared to non-SWE alternatives in the Climate Change context.

Introduction

SUDPLAN is an EU FP7 project under the Information Communication Technology programme (ICT-2009-6.4), running 2010-2012. The project responds to the calls target "ICT for a better adaptation to climate change" which asks for solutions that combine advanced environmental modelling and visualization, in support to EU initiatives like The "Shared Environmental Information System" (SEIS) [START_REF]EC: Communication from the commission to the council, the european parliament, the european economic and social committee and the committee of the regions -towards a shared environmental information system (seis) SEC(2008) 111SEC[END_REF] and The "Single Information Space in Europe for the Environment" (SISE) [START_REF] Hebek | Shared environmental information system and single information space in europe for the environment: Antipodes or associates?[END_REF].

The on-going implementation of the Infrastructure for Spatial Information in Europe directive (INSPIRE) [START_REF]EC: Directive 2007/2/ec of the european parliament and of the council of 14 march 2007 establishing an infrastructure for spatial[END_REF], the transition from research to operations of the Global Monitoring for Environment and Security (GMES) initiative [START_REF] Ep | Council: Regulation[END_REF], the development of a Shared Environmental Information System (SEIS), and the combination of all three as a European contribution to the Global Earth Observation System of Systems (GEOSS) initiative [START_REF]GEO: Geo 2009-2011 work plan[END_REF], are profoundly changing the design of environmental applications. In order to allow re-using of investments across usage areas, organisations and applications, the researchers and application developers are required to provide access to "their" data and other functional building blocks (e.g. processing, visualization) through standardized web service interfaces.

Sensor Web Enablement suite of standards developed by Open Geospatial Consortium (OGC SWE) [START_REF] Botts | Ogc sensor web enablement: Overview and high level architecture (07[END_REF] already provides much of the required functionality for sensors and sensor observation archives; the SANY Sensor Service Architecture (SensorSA) [START_REF]Specification of the sensor service architecture[END_REF] and the "Model Web" [START_REF] Geller | Looking forward: Applying an ecological model web to assess impacts of climate change[END_REF] envision the similar functionality for environmental models, and the newly coined "observation web" [START_REF] Havlik | From sensor to observation web with environmental enablers in the future internet[END_REF] extends these ideas to observations provided by humans.

Data, transport, and model runs in Service Oriented Architecture

Service-oriented architecture (SOA) is widely accepted as the paradigm of choice to loosely couple software components in distributed applications [START_REF]geographic information -services[END_REF][START_REF]Reference model for the orchestra architecture (rm-oa) v2 (rev 2.1). Best Practice OGC 07-024[END_REF]. Applied to numerical models, the SOA paradigm leads to idea of "Model Web", where model engines, as well as the resources required by these models are consequently encapsulated behind service interfaces and re-usable across a range of applications.

This section summarizes some of the key SOA requirenments. A more detailed discussion of this topic is given in ORCHESTRA RM-OA [START_REF]Reference model for the orchestra architecture (rm-oa) v2 (rev 2.1). Best Practice OGC 07-024[END_REF] and SANY fusion architecture documents [START_REF]Sany fusion and modelling architecture[END_REF] 1. Discovery of data and models With a notable exceptions of the RESTfull web services [START_REF] Rodriguez | Restful web services: The basics[END_REF] and the LinkedData approach [START_REF] Berners-Lee | Linked data[END_REF], the web services remain invisible for end users and web crawlers. Model Web resource discovery therefore requires special-purpose catalogs offering structured information on available resources. At the time of writing this paper, no fully functional generic solution for data and model discovery exists, but the future INSPIRE and GMES catalogues are expected to provide much of the required functionality. Comprehensive knowledge archive network (CKAN, http://ckan.net) search engine provides an interesting alternative to both Google and specialised catalogues for the Linked Data cloud. Data and service discovery is not on the SUDPLANs' research agenda, and therefore will not be further considered in this paper. 2. Formal definition of the model, required input data, parameters and output values (not shown in Fig. 1). SOA paradigm foresees re-using services and resources in applications they were not initially designed for. Consequently, each service needs provide formal self-description of its methods, as well as of the specific functionality provided by the service. Model web services should also provide the estimates about input-and output-data sizes and expected runtimes.

Handling of the large data sets

Model web services consume data from one or more sources on input side, process it and produce new data set(s) on the output side. Furthermore, the processing is controlled by parameters.

Examples of input-and output-data relevant to SUDPLAN include climaterelevant sensor observations, pollution and rainfall patterns. Both the input data consumed by models, and their output can be large compared to the network throughput and the capacity of data services to deliver the required data. Consequently, the architecture must provide a way to replicate the data required for the model runs and to keep the local copy synchronised.

An interesting discussion of the data replication and syncronization in OGC SWE networks has been given by Havlik et al. [START_REF] Havlik | Cascading and replicating the ogc sensor observation service[END_REF]. 4. Model execution SOA services are typically idle unless explicitly triggered to perform some action. Model web services are often trigered by providing a set of parameters, but the model execution may be also performed separately from model configuration. Model parameters define the temporal and spatial constraints, as well as the initial conditions for the model run. In the model web context, the parameters may also allow the users to choose one of the available processing algorithms, decide which input data to use, or to schedule the delayed model execution.

Progress monitoring and control

Some models are capable of calculating the results almost imediately. Other, including the SUDPLANs' downscalling services may require weeks to complete the task. This results in the need for monitoring the model progress and notifying the users of model status changes. 6. Download the results, which are also potential large datasets

The type and size of the model result differes in a wide range. From just a few numbers to a timeseries of 3D grids in the scale of 100GB. So it makes no sense to return each result to the client. Instead it is stored on the server site and the client gets informed of when and where (parts of) the results can be downloaded.

Selection of standards to be used by SUDPLAN

All data need to be self describing [START_REF]orchestra -an open source architecture for risk management[END_REF] which means that there is information about the values like unit of measurement, description of phenomena (e.g. a URN of a ontology), precision and uncertainity and methods of measurement in the form of sensor descriptions. The same is true for models as we expect to get more models over time and they should be usable without prior knowledge of any details. There is a lot of standards for data transport and remote service invocation. SANY SensorSA [START_REF]SANY -an open service architecture for sensor networks[END_REF] and Fusion Architecture [START_REF]Sany fusion and modelling architecture[END_REF] documents already foresee most of this. Within SUDPLAN we concentrated to the OGC service interfaces. The main reasons where:

-The OGC service interfaces are an accepted standard in the GIS comunity, and cover a large number of use-cases relevant to environmental usage area -All OGC specifications are freely available to everyone -All OGC service specifications share common data encodings (O&M -Observation and Measurement [START_REF]Opengis observations and measurements part 1 -observation schema[END_REF]) and descriptions (GML -Geography Markup Language [START_REF]Opengis geography markup language (gml) implementation specification[END_REF], SensorML -Sensor Model Language [START_REF]Opengis sensor model language (sensorml) implementation specification[END_REF]). -There is a lot of service interfaces to select from -Implementations of libraries and some ready to use services are already available as open source Now we will discuss which of the OGC interfaces are used in SUDPLAN and why we decided to use them.

Data transport: SOS, WCS, WFS, WMS?

Most of the data used in SUDPLAN fall into one of the following two categories:

timeseries of scalar values like e.g. rain data from one measurement station, and timeseries of field data, e.g. mean temperature changes over whole europe for the next 100 years, one field every 10 years, one value in the field representing a 100x100 km sqare over Europe.

As discussed in the previous section, the associated meta-information such as units, spatial and temporal references hs to be provided together with the data.

Consequently, most of the SUDPLAN data can be thought of as timeseries of observations [START_REF]SANY -an open service architecture for sensor networks[END_REF].

We needed to find a service interface that is generic enough to transport all out data but as specific as possible. Following OGC services were considered in SUDPLAN:

WMS -Web Map Server

The main goal of the WMS [START_REF]Ogc web map service interface[END_REF] is to deliver maps to be visualized by a client. There is a lot of clients available ranging from browser-based interfaces to complete GIS systems.

The focus of the maps is the visualization, so the information is rendered and deliverd as images. In SUDPLAN the results from one model is often the input to the next model so images are not the data format of our choice. Also images are not the best data representation when comparing different climate or planning scenarios or when doing some more sophisticated visualizations.

Therefore the use of WMS in SUDPLAN is limited to background maps and quick overviews of the model results. The best candidate for this are the precalculated Climate Scenario data, which are used to show SUDPLAN users what climate changes to expect.

WFS -Web Feature Service

A WFS [START_REF]Web feature service implementation specification[END_REF] provides GML-encoded data with geographic reference. This is a very powerfull service and many GIS systems can access a WFS to get data to render on a map.

WMS is often used to access shape files. While it would be possible to use WMS for accessing data in SUDPLAN, this standard is not optimised for handling timeseries of observations.

WCS -Web Coverage Service

The WCS [START_REF]Web coverage service (wcs) implementation specification[END_REF] is in many aspects similar to WMS.

Unlike WMS, the WCS return a grid of values suitable for further processing. The usage of WCS as a way to access coverages was discussed within SUD-PLAN. However, WCS is not optimised for timeseries of simple observations (unlike SOS). WCS remains an option for later extensions as at this early project state we tried to reduce complexity and implementation overhead. [START_REF]Opengis sensor observation service -implementation specification[END_REF] has been designed to provide access to sensor observations. Many sensors repeatedly observe the same phenomenon, so the result is typically a timeseries of observations, each associated to a time stamp. A model, or other processing entity, can be treated similarly to a sensor. Same is valid for the model input, output, parameters and identification information [12, ?].

SOS -Sensor Observation Service SOS

Although SOS appears to be the obvious service interface for data access in SUDPLAN, the encoding of continuous coverages has not been well-defined in OGC SWE version 1.0. SOS relies on O&M information model, and the SUD-PLAN approach for encoding continuous coverages, within limitations of the SOS 1.0 and O&M 1.0, is discussed in 3.3 section of this paper.

Model invocation: SPS, WPS?

Two of the OGC service specifications are suitable for initiation of remote program runs: Web Processing Service (WPS) and Sensor Planning Service (SPS).

WPS -Web Processing Service

The WPS, initially named "Geoprocessing Service", was first introduced 2005. The WPS v1.0 (2007) [START_REF]Opengis web processing service[END_REF] defines the web service interface and the mechanisms necessary to execute processes (analisys of georeferenced data) as well as to publish process dectiptions. The three WPS operations (GetCapabilities, DescribeProcess and Execute), providr enough flexibility to easily handle most processing use cases. Unfortunately, the WPS's flexibility of defining provider and domain specific input and output formats comes at the cost of the client which must support these formats.

SPS -Sensor Planning Service

The more capable and complex SPS v1.0 was introduced in 2007 [START_REF]Opengis sensor model language (sensorml) implementation specification[END_REF] with the goal to describe sensor platforms and to allow the planning and scheduling of complex tasks in the context of earth observation satellites. While the initial goal of SPS seems somewhat distant from our goal of runing models subsequent research has shown that SPS can be sucesfully used as a general purpose interface for models and data fusion services.

In comparison to the WPS the SPS standard is more structured, and provides pre-defined mechanisms for client notification (status and result availability). The GetFeasibility operation in the SPS standard allows for client feedback, on the viability of a processing operation, before committing to execution. The SUDPLAN requirements on model description with formal specifications of parameters, input data and results, can be fulfilled by both WPS and SPS. However, the requirements on scheduling, cancelling, and monitoring the progress of the model runs can only be fulfilled by SPS. Further advantage of the SPS over WPS lies in the built-in notification handling.

Usage of OGC services for model invocation SUDPLAN

O&M encoding of timeseries of fields

As mentioned earlier, based on the size of the data, a distinction is made between parameters for a model run and input data. While the parameters are of simple type and small in size the input data for a model is complex in nature and quite common of several GB in size. Because the model needs a fast access to the input data this data has to be uploaded to the model site. The user of the model does this (sometimes over institutional boundaries) through an implementation of the SOS interface. This interface is also used to enumerate and retrieve the model results. Model result data transfered through the SOS interface is encoded using the O&M [START_REF]Opengis observations and measurements part 1 -observation schema[END_REF] information model.

The O&M encoding is straight forward for timeseries of scalar values and used by many SOS implementations (52North [START_REF]Sensor observation service[END_REF], OOSTethys [START_REF]Sensor observation service[END_REF]). The encoding of timeseries of coverages is not that well defined. Observations and Measurements -Sampling Features (O&M-SF) [START_REF]Opengis observations and measurements part 2 -sampling features[END_REF] specification provides means to encode discrete coverages and provides dedicated elements to do so, but it lacks dedicated elements necessary to describe continuous coverages.

SANY "Fusion and Modelling Architectural Design" [START_REF]Sany fusion and modelling architecture[END_REF] document describes two methods for encoding coverages in O&M. Both are using the O&M-SF sa:SamplingSurface element. While there is no dedicated element in O&M-SF v1.0 to specify a grid on which the sampling takes place, both methodes describe the sampling points and grid by embeding its description in sub-elements of the sa:SamplingSurface, which was not designed for this purpose.

In SUDPLAN a further method has been considered for describing continuous coverages.The optional SOS method DescribeFeatureOfInterest allows for retrieval of the feature type description (xml schema) for a given feature of interrest. This allows us to introduce a new namespace containing additional Sampling Feature types. The new SamplingGrid type inherits from the sa:SpatiallyExtensiveSamplingFeatureType defined in O&M-SF and contains a gml:RectifiedGrid element that describes the rectified grid on which the sampling takes place. The advantage of the mentioned inheritance is that the sampledFeature relation is retained. SUDPLAN SamplingGrid schema and one Sampling-Grid instance example are shown below.

SamplingGrid schema:

Using UncertML to describe statistical data

The descriptive model language Uncertainty Markup Language -UncertML [START_REF] Williams | Uncertainty markup language (uncertml)[END_REF] developed by the INTAMAP project can be used to encode the accuracy of the observation collection. SUDPLAN rainfall downscaling service generates a 2D table of the predicted precipitation values for the total seasonal accumulation (TOT), maximum 30-min intensity (MAX) and frequency of occurrence (FRQ). Because of the statistical characteristics of this data, UncertML could be used to encode it, similar to the way uncertainities in sensor data and model outputs were encoded in SANY.

This would mean treating this data as description of the timeseries of rainfall, not as an independent result. At the same time this data has the characteristics of a time series, meaning that it provides the above mentioned statistical information for every season. At this point no decission has been taken on wether in SUDPLAN this data should be encoded as observations (using O&M), or as observation uncertainities (using UncertML).

Using SensorML to describe models and required parameters

The SOS and SPS interfaces provide process descriptions encoded in in SensorML, through describeSensor operation. In SUDPLAN, the process is a models and can be described as Non-Physical (pure) process. The information provided in the form of a SensorML document can be quite extensive encompassing model inputs, parameters, outputs, the model algorithm itself and details of the implementation module. Currently, our use-cases require descriptions of constant model parameters necessary for the human interpretation of the model results. This includes: model identification, responsible party, input and output model. Our processing services act as clients to several Sensor Observation Services.

The following simplified SensorML document shows basic model identification as well the inputs and outputs of the rain downscaling model used in SUDPLAN. The model takes as input a SOS offering name containing the historical rain measurements as well as a future timestamp around which the downscaling results are generated. The model outputs a timeseries of coverages with the result model described in the Downscaled rain named element. <sml:SensorML xmlns:sml="http://www.opengis.net/sensorML/1.0.

Rain timeseries downscaling

SUDPLAN rain timeseries Downscaling Service provides information on the local rainfall patterns for the next 100 years.

The input data is a historical timeseries of scalars, typically ten years of measured precipitation, one value every 5 minutes. This gives about one million values. Three parameters have to be provided by user for each model run: the name of the uploaded timeseries, the climate scenario to use, and the date for which a new timeseries shall be calculated1 .

The runtime of the model is short, just some minutes. The result is a timeseries with the same characteristics as the input timeseries, just moved into a possible future. Additional results are aggregated timeseries (e.g. one value every 30 minutes) and statistical information on the created data.

Air quality downscaling

quality Downscaling Service is the most important of the SUDPLAN Common Services. The model behind this service is able to perform local air quality downscaling on local emission data. The input is a field of expected emissions of greenhouse gases over an area of interest, typically a city. The parameters for this service are the climate scenario to use, the time interval and the area for which the air quality has to be calculated.

The runtime on a background supercomputer is expected to be around 10 days for a typical model run, and the result size is about 100GBytes. The model user has the possibility to download some result subsets, e.g. timeseries of grids, one value every 10 years or a timeseries for one location with higher temporal resolution. Available phenomena include temperature, precipitation, NO2 and more.

Software used

SUDPLANS aims to provide affordable downscaling services with standardised interfaces, and usable by every city in Europe. The main software components developed in SUDPLAN are:

Common Services

Most of the SUDPLAN Common Services already existed before the SUDPLAN project. These services are propriatery and executed on specialised supercomputers. These services are accessed through standardized interfaces implemented by AIT using the open source TimeSeries Toolbox [START_REF]The time series toolbox overview[END_REF]. This implementation provides a wrapper around the models providing SOS interfaces for the transfer of data and SPS interfaces for executing and monitoring the models.

In addition, the WMS provides quick overviews of the model results. SUDPLAN uses the GeoServer [START_REF]Geoserver web map service documentation[END_REF] open source WMS implementation.

Scenario Management System

The Scenario Management System (SMS) is the main SUDPLAN user interface providing means to control models, retrieve and visualise results. SMS is based on cismet's CIDS system [START_REF][END_REF]. Both, CIDS as well an the SUDPLAN specific extensions are available under an open source licence.

Local models

SUDPLAN foresees two ways to extend the functionality of the system with additional local models: through SMS extensions, and through exposing of the local models through an SPS interface implementation analogously to the Common Services implementations described above.

Depending on the data type, a WFS (e.g. digital elevation model), a WMS (Aerial photos for visualisation) or a SOS (Sensor data) can be used. Open source implementations for all of these services are available from various sources.

Conclusion and outlook

This paper describes the current status of the SUDPLAN Model Web Implementation. SUDPLAN implementation bases of the OGC SWE 1.0 standards and on the SANY SensorSA idea of interfacing the models with SOS and SPS service interfaces. Our results confirm the suitability of the OGC SOS and OGC SPS services and related data encodings from the OGC SWE 1.0 standard suite for Climate Change related models and model results. SUDPLAN software related to interfacing of models and accessing the model results is avaliable as open source on ts-toolbox.ait.ac.at web site. Some of the shortcomings mentioned in the paper have been adressed in SWE 2.0 development, in particular, the O&M 2.0 specification of the SamplingFeature. The SamplingGrid introduced in SUDPLAN provides an alternative method for describing the SamplingFeature (grid) of a continuous coverage. This method is superior to grid-encoding methods proposed in SANY, as it both retains the SampledFeature relation to the FeatureOfInterest and avoids the need of explicitly specifying all points of the grid. Furthermore, we believe that SamplingGrid is better aligned with O&M 2.0 specifications and provides a valid altenative to WCS encoding of continuous coverages.

SUDPLAN consortium is discussing the feasibility of using UncertML to describe statistic features of the downscaling results, and welcomes input on this topic.

Fig. 1 .

 1 Fig. 1. The overall picture of a model invocation

Fig. 2 .

 2 Fig. 2. Model invocation details showing OGC interface usage

4

  Different model types used in SUDPLANThis section provides three examples of the SUDPLAN Common Services[START_REF]Common services[END_REF] illustrating the bandwith of the model characteristics. We concentrate on: 1. Required input data type and size 2. Need of parameters, 3. Expected runtime, 4. Type and size of result 4.1 Climate Scenario data Climate Scenario Common Service provides access to precalculated results of the climate change model. The result are timeseries of fields, typically about 51x51 values, one grid per 10 years over a time period of 140 years. Each field can contain some values (e.g. Temp, NO2). The entire dataset is about (51 * 51 * 13 decades * 7 different observed properties) 250000 values.

Fig. 3 .

 3 Fig. 3. Access Climate Scenario data

Fig. 4 .

 4 Fig. 4. Using downscaled rain timeseries as input a model

The location for which the future timeseries should be calculated is taken from the input timeseries.

Acknowledgments

The work leading to the results presented in this paper has been partially funded through FP6 Integrated Project Sensors Anywhere (SANY; FP6 Contract number 033564; http://sany-ip.eu/) as well as through the FP7 Sustainable Urban Development Planner for Climate Change Adaptation project (SUDPLAN; EC grant agreement nr. 247708; http://sudplan.eu).