Ioannis Athanasiadis | Γιάννης Αθανασιάδης
Environmental Modelling & Software
Filed Under (Conference papers) by I N Athanasiadis on 26-04-2007
In Proceedings of the 3rd International Workshop on Semantic Web Enabled Software Engineering, 4th European Semantic Web Conference, (E. F. Kendall and others, ed.), pp. 16-30, Innsbruck, Austria, 2007.
Abstract Domain-specific conceptualizations are increasingly specified as formal ontologies, as part of ongoing efforts for enabling the semantic web. However, experience has shown that semantic models and their incarnations into OWL structures, though powerful for expressing complex abstractions, remain difficult to utilize in conventional software projects. In this paper we present our work for coupling ontologies with conventional domain-centric data models and object-relational persistence. The Semantic Rich Development Architecture methodology is specified for assisting the software developer to build-up enterprise applications, starting from a formal domain specification expressed in OWL. This way, a knowledge-based enterprise development environment is introduced that demonstrates the benefits of coupling ontologies with software development standards.
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Filed Under (Conference papers) by I N Athanasiadis on 26-04-2007
In Proc. of the 31th IEEE Annual International Computer Software and Applications Conference (COMPSAC), pp. 341-346, Beijing, China, 2007.
Abstract To put in practice knowledge-based software engineering practice we need frameworks that enable the programmer to integrate semantic-rich approaches in conventional software development process. In this work, we present how a knowledge base can be smoothly integrated with conventional domain-centric data models, as Enterprise Java Beans and object-relational mapping toolkits, as hibernate. We present a clear pathway for the software developer, starting from a domain ontology, how to generate both enterprise Java beans source code and hibernate object-relational mappings. In this way, a semantic-rich enterprise development environment is specified, that combines both the benefits of using ontologies and software development standards.
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Filed Under (Conference papers) by I N Athanasiadis on 26-04-2007
In 2007 European Simulation Interoperability Workshop, pp. 60, Genoa, Italy, 2007.
Abstract The size and scope of simulation models is constantly growing in order to keep up with the requirements imposed by increasingly complex scenario analyses. Moreover, advances in the performance of simulation hardware, of communication software, and development frameworks, also offer unprecedented opportunities. As a consequence, the complexity of the design, implementation and deployment of simulation models is also increasing, up to a point that it might soon become unmanageable. We describe an approach aimed at taming such complexity based on the use of ontologies to structure modelling and simulation knowledge, which can be manipulated through a knowledge manager to facilitate the development of models and tools for simulation. We present two case studies and demonstrations of this approach. The first targets the integrated assessment of the common agricultural policies of the EU where models pertaining different domains (environmental, social, economic) and different scales (local, regional, continental) must be integrated. The second focuses on the integrated design of energy supply systems, where alternative models for energy supply systems must be integrated in order to select the best combination to produce energy minimizing costs and environmental impacts.
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Filed Under (Conference papers) by I N Athanasiadis on 26-04-2007
In Seventh Int’l Symposium on Environmental Software Systems (ISESS-05), Prague, Czech Republic, 2007.
Abstract In this paper, the software development and test applications of a component-based generic farm system simulator is presented. The Farm System Simulator (FSSIM) developed within the EU FP6 SEAMLESS project is an integrated modelling system developed to assess the economic and ecological impacts of agricultural and environmental policies and technological innovations. Based on the semantic link of biophysical and micro-economic models, FSSIM seeks to describe the behaviour of the farmer at the farm level given specific biophysical, socio-economic and policy constraints, in order to analyze in an integrated manner the behaviour of the whole farming system. FSSIM is a modular system which involves a mathematical programming model (FSSIM-MP), and an agricultural management module (FSSIM-AM) that has several model components to generate the technical coefficients needed by FSSIM-MP. The respective model component of FSSIM-MP and those of FSSIM-AM are developed in different modelling environments by using C, Java and GAMS, while data is stored in relational databases. Model assumptions, interfaces and causal chains, along with available data sources are specified in a declarative fashion, serving as the building blocks of a semantic-aware integration framework based on ontology-enabled knowledge base. A set of ontologies describing model components and databases for FSSIM has been built. Ontologies set up clear definitions for loosely integrating models in an open environment. In this way, model components are approached as autonomous agents confronting to open interfaces and strict contracts, and are automatically integrated into the FSSIM framework through OpenMI+ pulling model linking approach. An application of the FSSIM modules to different farm types within Midi-Pyrenees region is given as a case study.
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Filed Under (Conference papers) by I N Athanasiadis on 15-02-2007
In Information Technology in Environmental Engineering (ITEE 2007), (J. Marx Gómez, M. Sonnenschein, M. Müller, H. Welsch and C. Rautenstrauch, ed.), pp. 417-432, Oldenburg, Germany, 2007.
Abstract Farming Systems Research studies agricultural systems and their interaction with the natural environment and ecosystems. Agro-ecosystems are highly complex due to the many feedbacks between natural processes, high geographical diversity and human factors involved both as the farmer’s decisions at farm household level and as the policy implementations at regional, national or European levels. This paper presents a novel approach for developing an Agricultural Management Definition Module (AMDM), by exploiting ontologies and semantic modeling. Specifically, a declarative approach has been utilized for conceptualizing farming systems and the management alternatives of a farm household. This conceptual model has been implemented as an ontology that ultimately has been used as the basis for software development and integration. This paper presents in detail the methodology used for developing AMDM and a real-world installation, part of the SEAMLESS integrated project.
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Filed Under (Journal articles) by I N Athanasiadis on 11-02-2007
In International Journal of Product Lifecycle Management, 2(2):173-186, 2007.
Abstract Software agent technology has matured enough to produce intelligent agents, which can be used to control a large number of concurrent engineering tasks. Multi-agent systems are communities of agents that exchange information and data in the form of messages. The agents’ intelligence can range from rudimentary sensor monitoring and data reporting, to more advanced forms of decision-making and autonomous behavior. The behavior and intelligence of each agent in the community can be obtained by performing data mining on available application data and the respected knowledge domain. We have developed Agent Academy, a software platform for the design, creation, and deployment of multiagent systems, which combines the power of knowledge discovery algorithms with the versatility of agents. Using this platform, we illustrate how agents, equipped with a data-driven inference engine, can be dynamically and continuously trained. We also discuss three prototype multi-agent systems developed with Agent Academy.
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Filed Under (Conference papers) by I N Athanasiadis on 11-02-2007
In Workshop on Social and Collaborative Construction of Structured Knowledge at 16th International World Wide Web Conference (WWW2007), (N. Noy, H. Alani, G. Stumme, P. Mika, Y. Sure and D. Vrandecic, ed.), Banff, Alberta, Canada, 2007.
Abstract In this paper, we describe collaborative efforts between a knowledge representation team and a community of scientists and information managers in developing knowledge models for ecology and environmental science. Formal, structured approaches to knowledge representation used by the team (e.g., ontologies) are informed by unstructured approaches to knowledge representation already in use by the community. Observations about the process of interaction between the team and the community are used to generate a set of technical requirements of a supporting system. These requirements are being used to develop a prototype system based on the ThinkCap collaborative knowledge portal toolkit.
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Filed Under (Book chapters) by I N Athanasiadis on 02-02-2007
In Computational Intelligence Based on Lattice Theory, (V. G. Kaburlasos and G. X. Ritter, ed.), pp. 175-193, 2007.
Abstract This chapter introduces a rule-based perspective on the framework of fuzzy lattices, and the Fuzzy Lattice Reasoning (FLR) classifier. The notion of fuzzy lattice rules is introduced, and a training algorithm for inducing a fuzzy lattice rule engine from data is specified. The role of positive valuation functions for specifying fuzzy lattices is underlined and non-linear (sigmoid) positive valuation functions are proposed, that is an additional novelty of the chapter. The capacities for learning of the FLR classifier using both linear and sigmoid functions are demonstrated in a real-world application domain, that of air quality assessment. To tackle common problems related to ambient air quality, a machine learning approach is demonstrated in two applications. The first one is for the prediction of the daily vegetation index, using a dataset from Athens, Greece. The second concerns with the estimation of quartely ozone concentration levels, using a dataset from Valencia, Spain.
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Filed Under (Journal articles) by I N Athanasiadis on 18-01-2007
In Engineering Applications of Artificial Intelligence, 20(8):1097-1111, 2007.
Abstract The task-oriented nature of data mining (DM) has already been dealt successfully with the employment of intelligent agent systems that distribute tasks, collaborate and synchronize in order to reach their ultimate goal, the extraction of knowledge. A number of sophisticated multi-agent systems (MAS) that perform DM have been developed, proving that agent technology can indeed be used in order to solve DM problems. Looking into the opposite direction though, knowledge extracted through DM has not yet been exploited on MASs. The inductive nature of DM imposes logic limitations and hinders the application of the extracted knowledge on such kind of deductive systems. This problem can be overcome, however, when certain conditions are satisfied a priori. In this paper, we present an approach that takes the relevant limitations and considerations into account and provides a gateway on the way DM techniques can be employed in order to augment agent intelligence. This work demonstrates how the extracted knowledge can be used for the formulation initially, and the improvement, in the long run, of agent reasoning.
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Filed Under (Book chapters) by I N Athanasiadis on 10-01-2007
In Agent and Web Service Technologies in Virtual Enterprises, (N. Protogeros, ed.), pp. 256-266, Hershey, PA, USA, 2007.
Abstract This chapter introduces a virtual enterprise architecture for environmental information management, integration and dissemination. On a daily basis, our knowledge related to ecological phenomena, the degradation of the natural environment and the sustainability of human activity impact, is growing as a consequence raises the need for effective environmental knowledge exchange and reuse. In this work, a solution among collaborating peers forming a virtual enterprise is investigated. Following an analysis of the main stakeholders, a service-oriented architecture is proposed. Technical implementation options, using Web services or software agents, are considered and issues related to environmental information management, ownership and standardization are discussed.
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