Ioannis Athanasiadis | Γιάννης Αθανασιάδης
Information Technologies in Environmental Engineering
Filed Under (Conference papers) by I N Athanasiadis on 10-11-2009
In Workshop on Ontology-Driven Software Engineering at OOPSLA, Orlando, FL, USA, 2009.
Abstract This paper investigates how to use an idiomatic OWL/RDF model as a specification language for delivering Domain Object Model with relational persistence. It presents a systematic translation of a subset of OWL/RDF constructs to object structures with a relational database back-end. The presented framework has been developed as a plugin for the Protege ontology editor, and it has been evaluated against a benchmark of semantic repositories with promising results.
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Filed Under (Conference papers) by I N Athanasiadis on 03-08-2009
In 3rd Intl Conf on Metadata and Semantics Research (MTSR’09), (F. Sartori, M. A. Sicilia and N. Manouselis, ed.), pp. 282-293, 2009.
Abstract This paper presents a set of ontologies developed in order to facilitate the integration of a variety of combinatorial, simulation and op- timization models related to agriculture. The developed ontologies have been exploited in the software lifecycle, by using them to specify data communication across the models, and with a relational database. The Seamless ontologies provide with definitions for crops and crop products, agricultural feasibility filters, agricultural management, and economic valuation of crop products, and agricultural and environmental policy, which are in principle the main types of data exchanged by the models. Issues related to translating data structures between model program- ming languages have been successfully tackled by employing annotations in the ontology.
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Filed Under (Books) by I N Athanasiadis on 04-06-2009
Proceedings of the 4th ICSC Symposium on Information Technologies in Environmental Engineering.
Edited by Ioannis N. Athanasiadis, Pericles A. Mitkas, Andrea-Emilio Rizzoli and Jorge Marx-Gomez
From the back cover: Information technologies have evolved to an enabling science for natural resource management and conservation, environmental engineering, scientific simulation and integrated assessment studies. Computing plays a significant role in every day practices of environmental engineers, natural scientists, economists, and social scientists. The complexity of natural phenomena requires interdisciplinary approaches, where computing science offers the infrastructure for environmental data collection and management, scientific simulations, decision support documentation and reporting.
Ecology, environmental engineering and natural resource management comprise an excellent real-world testbed for IT system demonstration, while raising new challenges for computer science. Complexity, uncertainty and scaling issues of natural systems form a demanding application domain for sensor networks and earth observation systems; modelling, simulation and scientific workflows, data management and reporting, decision support and intelligent systems, distributed computing environments, geographical information systems, heterogeneous systems integration, software engineering, accounting systems and control systems.
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Filed Under (Journal articles) by I N Athanasiadis on 01-05-2009
In Environmental Modelling and Software, 24(5):577-587, 2009.
Abstract Models, and to a lesser extent datasets, embody sophisticated statements of ecological knowledge. Yet, the knowledge they contain is rarely self-contained enough for them to be understood and used – by humans or machines – without the ecologist’s mediation. This severely limits the options in reusing ecological models and connecting them to dataset or other models. The notion of “declarative modelling” has been suggested as a remedy to help design, communicate, share and integrate models. Yet, not all these objectives have been achieved by declarative modelling in its current implementations. Semantically-aware Ecological Modelling (SEM) is a way of designing ecological datasets and models based on the independent, standardized formalization of the underlying ecology, resulting from merging the rationale of declarative modelling with the most recent advances in computer science and integrative visions such as the Semantic Web. In this paper, we discuss the present and preview the future of semantic modelling in Ecology: from the semantically-mediated integration approach, where formal knowledge is the key to automatic integration of datasets, models and analytical pipelines, to the more far-fetched but promising “strong” approach where the knowledge is the key to not only to integration, but also to overcoming scale and paradigm differences and to novel potentials for model design and automated knowledge discovery.
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Filed Under (Journal articles) by I N Athanasiadis on 22-04-2009
In Environmental Modelling and Software, 24(12):1491-1500, 2009.
Abstract Integrated Assessment and Modelling (IAM) provides an interdisciplinary approach to support ex-ante decision-making by combining quantitative models representing different systems and scales into a framework for integrated assessment. Scenarios in IAM are developed in the interaction between scientists and stakeholders to explore possible pathways of future development. As IAM typically combines models from different disciplines, there is a clear need for a consistent definition and implementation of scenarios across models, policy problems and scales. This paper presents such a unified conceptualization for scenario and assessment projects. We demonstrate the use of common ontologies in building this unified conceptualization, e.g. a common ontology on assessment projects and scenarios. The common ontology and the process of ontology engineering are used in a case study, which refers to the development of SEAMLESS-IF, an integrated modelling framework to assess agricultural and environmental policy options as to their contribution to sustainable development. The presented common ontology on assessment projects and scenarios can be reused by IAM consortia and if required, adapted by using the process of ontology engineering as proposed in this paper.
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Filed Under (Journal articles) by I N Athanasiadis on 31-03-2009
In Environmental Modelling and Software, 24(11):1264-1273, 2009.
Abstract The Meteorological Service of Cyprus operates a Doppler radar at the mountainous region of the island. Data-streams recorded by the radar are used for weather forecasting and, especially, for identifying oncoming precipitation incidents and issuing (potential) warnings. However, the continuous processing and evaluation of radar data requires significant efforts by the meteorologists, both for data processing, storage, and maintenance, as well as for data interpretation and visualization. To assist meteorologists and to automate a large part of these tasks, we have designed and developed Abacus, a multi-agent system for managing radar data and providing decision support. Abacus’ agents undertake data-management and visualization tasks, while they are also responsible for extracting statistical indicators and assessing current weather conditions. In addition, Abacus’ agents can identify potentially hazardous incidents, disseminate preprocessed information over the web, and enable warning services provided via email notifications. In this paper, Abacus’ agent architecture is detailed and agent communication for information diffusion is discussed. Focus is also given on the fully customizable logical rule-bases used for agent reasoning required in decision-support. The platform has been tested with real-world data from the Meteorological Service of Cyprus.
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Filed Under (Book chapters) by I N Athanasiadis on 07-02-2009
In Advanced Agent-Based Environmental Management Systems, (U. Cortés and M. Poch, ed.), pp. 119-138, 2009.
Abstract This chapter presents a unifying methodology for developing environmental information systems with software agents. Based on the experience reported in recent literature, we abstract common requirements of environmental information systems into agent types, combine state-of-the-art tools from computer science, service-oriented software engineering and artificial intelligence domains, as software agents and machine learning, and illustrate their potential for solving real-world problems. Specifically, two generic agent types are specified that behave as information carriers and decision makers, which provide an appropriate abstraction for deployment with added-value services in environmental management information systems. A concrete pathway for applying these instruments throughout the software lifecycle of an environmental management information system is outlined, along with suggestions for software specification and deployment. The method is demonstrated in two application domains: one for air quality assessment and another for meteorological radar data surveillance.
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Filed Under (Journal articles) by I N Athanasiadis on 22-01-2009
In Environmental Science and Policy, 12(5):573-587, 2009.
Abstract A major bottleneck for data-based policy making is that data sources are collected, managed, and distributed by different institutions, residing in different locations, resulting in conceptual and practical problems. The use of dispersed data for agricultural systems research requires the integration of datasources, which means to ensure consistency in data interpretations, units, spatial and temporal scales, to respect legal regulations of privacy, ownership and copyright, and to enable easy dissemination of data. This paper describes the SEAMLESS integrated database on European agricultural systems. It contains data on cropping patterns, production, farm structural data, soil and climate conditions, current agricultural management and policy information. To arrive at one integrated database, a shared ontology was developed according to a collaborative process, which facilitates interdisciplinary research.The paper details this process, which can be re-used in other research projects for integrating data sources.
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Filed Under (Conference papers) by I N Athanasiadis on 13-05-2008
In Proceedings of the Fourth International Congress on Environmental Modelling and Software (iEMSs 2008), (M. Sànchez-Marrè, J. Béjar, J. Comas, A. E. Rizzoli and G. Guariso, ed.), pp. 719-726, Barcelona, Spain, 2008.
Abstract Integrated Assessment and Modelling (IAM) can be used to assess socio-economic and environmental indicators, which generally require the linkage of models from different domains. To integrate a set of different models for an IAM, the data required by each of the models as inputs from a range of data sources also needs to be consistently integrated. This paper describes the process of development of a database integrating different data sources for an IAM project, and the human factors involved in the process of reaching consensus across peers with clashing requirements and needs. We adopted a structured process using a shared ontology as a means to one integrated relational database serving a set of models of a highly multi-disciplinary nature. The relational database covers data on agricultural systems, e.g. soil, climate, farm, agricultural management and agricultural policy data. The integrated database has been coupled to a range of quantitative models. The database schema and the shared ontology are distinct products that can be reused for or extended by other IAM projects requiring a similar set of data. It is recommended for any IAM project in which several models are coupled to adopt an explicit, collaborative and iterative process to specify an adequate data structure for storing data used in the project. For such a process to succeed it has to focus on the relevant domain knowledge captured across the data sources and this paper offers a proposal for such a process.
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Filed Under (Conference papers) by I N Athanasiadis on 08-05-2008
In Int’l Conf. on Impact Assessment of Land Use Changes, (O. Dilly and K. Helming, ed.), pp. 66, Berlin, Germany, 2008.
Abstract Integrated Assessment Modelling tackle complex problems through the integration of data-intensive models, which pose great challenges for the management of data, data sources and connections between models and their data. In SEAMLESS we have integrated different data-sources into one common data-schema, represented in both an ontology and a relational database. This paper presents four aspects of this work: (a) the use of ontologies and ontology engineering to create a shared conceptual model, (b) the generation of relational data schema from the shared conceptual model, (c) the processing of data sources to populate the database, including the adaptation of data to a common spatial framework and aggregating source data to suitable typologies. (d) the access of the models to the data in the database through the ontology. Through the use of ontology, ontology engineering and relational databases, the first Pan-European database on soil, climate, farm and agricultural management was created that is directly accessible for the models operating in SEAMLESS. The database holds data on model inputs and model outputs as well as contextual data for the assessments. For our developments only open source tools were used, so the ontology has been built in Protégé and the database schema generated through Hibernate. The data are stored in the relational database management system PostgreSQL running on a Linux server. To support visualization of results PostGIS functionality is added to a PostgreSQL database and Geoserver is used to provide Web Mapping and Web Feature services.
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Internet for peace
Environmental Modelling & Software
