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In the new era of knowledge economy, knowledge and the processes to generate and manage it are considered as the most valuable assets of an organisation in the competitive business environment. Over the last years, intensive discussions have taken place about the importance of Knowledge Management for the whole society, except from the business world. Today, there is a consensus among researcher's and practitioner's communities that the challenges facing modern societies, call for development strategies that are knowledge-based. In this context, the theme of Knowledge Cities (KCs) came to the front. The review of literature reveals that the process of designing Knowledge-Based Development (KBD) strategies for KCs is complex and not appropriately supported by decision support methodologies and/or intelligent systems. In this paper, the authors, based on a previously presented methodology for the formulation of a KBD strategy for KCs, propose an AI-based decision support system for designing such strategies, by selecting and prioritising the most appropriate interventions and actions. The system consists of two sub-systems: the first (developed using the technology of Expert Systems) assess the necessity of a particular intervention and proposes its most appropriate form. The second prioritises the selected interventions based on Multi-Criteria Decision Making. The authors also present the successful preliminary results of the systems pilot application to a Greek municipality.

Real Estate valuation in urban areas is a very difficult task that has absorbed the interest of many academics in the past years. Many qualitative and quantitative variables affect the value of an estate in urban areas. As a result, multivariate models are more suitable in the appraisal process. One of the most common approaches is multiple linear regression technique (MLR) that is always used as a benchmark in various studies. A very promising way of dealing with uncertainty in real estate analysis and producing sufficient evaluations is the use of Artificial Neural Networks (ANNs). Also, an expert method combining the strengths of MLR and ANN is tried out successfully. The purpose of this study is to compare these approaches on the basis of the data from the Attica urban area in Greece.

The main scope of this paper is the implementation of a method for production scheduling, using advanced model checking tools. This method makes use of timed automata to model complex production scheduling problems such as the job-shop and the open-shop scheduling problems. By modelling scheduling problems using timed automata, feasible schedules correspond to paths in the automata, while finding the optimum schedule corresponds to finding the shortest path in an automaton. Several algorithms and heuristics have been proposed for finding the shortest paths in timed automata, which are mainly based on the implementation of graph algorithms that search the transition graph. In our work, an advanced model design and checking tool, called UPPAAL, has been used to implement and test the above method on a number of different models and to show the effectiveness of such an approach in terms of finding optimal or near to optimal, schedules in polynomial time, even for large-scale problems.

In today's fierce business competition, innovation fuels organisational growth, drives future success and is the engine that allows businesses to sustain their viability in the new knowledge-based economy. Innovation does not require genius, but it does require a system-wide dedication to pursue unique opportunities. Since the specific requirements and critical factors needed to support innovation are only recently being explored, this paper seeks to analyse the value of effective Knowledge Management (KM) leading to innovation. KM has recently received considerable attention in the computer information systems community and is continuously gaining interest in industry, enterprises and academia. As we are moving into an era of 'knowledge capitalism', knowledge management will play a fundamental role in the success of today's businesses. This paper presents the key aspects of the role of KM in the enterprises, explores the relationship between KM and innovation, and gives answer to the difficult question: 'Can effective KM create more successful innovation and how?'

The effective use of Information Technology (IT) to help modern companies improve service quality, financial performance, customer satisfaction and productivity is a very crucial issue nowadays. Intelligent solutions, based on Expert Systems (ES), to solve complicated practical problems in various sectors are becoming more and more widespread. However, the real success of applied expert systems in the improvement of companies' performance is being investigated by the research community. In this framework, the primary objective of this paper is to present what is important for a successful ES application in terms of quality and what kind of mistakes can be made. Our analysis is based on an empirical evaluation of three ES applications that have successfully been in industrial use for a long time and in the development of which we have been personally involved. The key findings are expressed as 14 hypotheses for a successful ES. The support of each application to the hypotheses is discussed.

The major advantage of electronic markets over nonelectronic traditional market places is that trading partners use more sophisticated methods, rules and systems of information exchange. The internet has inevitably boosted this advantage over the last decade, making electronic markets the dominant 'playing field' for various industrial and business activities. Forecasting is one of them; it is important to firms because it can help ensure that resources are used effectively. In the market there are a number of off-the-self system products that provide forecasts. The new trend of moving traditional software packages to web services has pushed forecasting to a new dimension – e-forecasting, as called by the authors. In this paper we explore the challenges and opportunities arising from e-forecasting. Two very fertile e-areas for possible application are discussed in detail: e-government and forecasting community portals.

Over the past several years, there have been intensive discussions about the importance of Knowledge Management (KM) within our society. As we are moving into an era of "knowledge capitalism", the management of knowledge is promoted as an important and necessary factor for organisational survival and maintenance of competitive strength. During the last 15 years, KM has changed from one generation to the next through constant improvements and new perspectives. Many researchers have presented methodologies, frameworks, technologies and have discussed various KM theoretical and practical issues in several sectors, including healthcare. E-health and KM-based health are still in an early state of evolution, and it is only recently that researchers decided to intensify their efforts in these fields. In this context, this paper aims to review the current status quo, analyse key issues to which researchers should pay attention and contribute to researchers' dilemma solving about where future research should be focused.

Intelligent computer applications (ICA) to solve complicated practical problems in various sectors are becoming more and more widespread nowadays. This paper surveys the application of intelligent systems in the energy sector using a literature review and classification of papers from 1990 to 2003 with keyword index in order to explore how intelligent methodologies and applications have developed in this period. Based on the scope of 247 papers of intelligent energy technologies this paper surveys and classifies ICA in the energy sector using the four categories of: expert systems, neural networks, genetic algorithms, fuzzy-logic-based systems, together with their applications for different problem domains in the specific sector. The paper concludes by sharing thoughts and estimations on ICA future prospects in the energy sector.

Over the past several years there have been intensive discussions about the importance of knowledge management (KM) within our society. As we are moving into an era of "knowledge capitalism", the management of knowledge is promoted as an important and necessary factor for organisational survival and maintenance of competitive strength. During the last 15 years, KM has changed from one generation to the next through constant improvements and new perspectives. Many researchers have presented methodologies, frameworks, technologies and have discussed various KM theoretical and practical issues in several sectors, including the energy sector. This paper aims to analyse the necessity of the application of KM in the energy sector, discuss key issues and managerial implications and describe how the KM process can be implemented in this sector.

Good governance is a concept that has recently come into regular use in political science and public administration and appears to be member of a wider group of terms such as democracy, civil society, human rights, social development. In addition, it is only recently that government policy makers have started looking at the potential of applying the tools and techniques of e-commerce to the tasks of government. In parallel, during the last 15 years, KM has changed from one generation to the next through constant improvements and new perspectives. Many researchers have presented methodologies, frameworks, technologies and have discussed various KM theoretical and practical issues in several sectors, including public administration. Yet, e-government and KM-based public administration are still in an early state of evolution, and it is only recently that researchers decided to intensify their efforts in these fields. In this context, this paper aims to present a conceptual analysis of KM in e-government, discuss key issues related to the application of KM in e-government to which researchers should pay attention and describe a Knowledge Model as a basis for future research.

Advances in telecommunications, automated processes, web technologies and wireless computing are already forcing dramatic changes in a variety of sectors, ranging from business and industry to education and health. Yet, the electronic business space, in a broader sense, is still in a relatively early state of evolution, and it is only recently that policy makers have started looking at the potential of applying the tools and techniques of e-commerce to the tasks of other sectors. The use of the internet as a source of health information and connectivity between healthcare providers and consumers has increased interest in e-health. E-health offers the rich potential of supplementing traditional delivery of services and channels of communication in ways that extend the healthcare organisation's ability to meet the needs of its patients. To date, some e-health applications have improved the quality of healthcare, and later they will lead to substantial cost savings. However, e-health is not simply a technology but a complex technological and relational process. In this sense, practitioners and researchers who want to successfully exploit e-health need to pay attention to various pending issues that have to be addressed. The aim of this paper is to propose a novel taxonomy for e-health research in the new millennium by instantaneously presenting the current status with some major themes of e-health research.

Companies deal with many decision-making processes whose impact on the global performance can be very strong. As a consequence, the role of decision support systems (DSSs) within the organisation is critical. Considering the imprecise or fuzzy nature of the data in real-world problems, it becomes obvious that the ability of managing uncertainty turns to be a crucial issue for a DSS. Fuzzy logic (FL) is a method for understanding, quantifying and dealing with vague, ambiguous and uncertain characteristics, ideas and judgments. In more specific terms, what is central about fuzzy logic is that, unlike classical logical systems, it aims at modelling the imprecise modes of reasoning that play essential role in the remarkable human ability to make rational decisions in an environment of uncertainty and imprecision. In this framework, this paper aims at promoting the integration of fuzzy logic into DSSs for the benefit of decision-makers. It discusses the key role of FL in DSSs, presents new applications of FL in DSSs in various sectors and identifies new challenges and new directions for further research. This review reveals that, although still regarded as a new methodology, FL is shown to have matured to the point of offering real practical benefits in many of its applications.

Good governance is a concept that has recently come into regular use in political science and public administration and appears to be a member of a wider group of terms such as democracy, civil society, human rights, social development. In addition, it is only recently that government policy makers have started looking at the potential of applying the tools and techniques of e-commerce to the tasks of the government. Similar to the dramatic changes in e-commerce and e-business, the e-government revolution offers the potential to reform the public sector and improve the relationship between citizens and the government. This paper aims to highlight the importance of the emergence of e-government. It gives insights to the scope of developments in this emerging field, discusses key issues and implications and reviews specific case studies and success stories in order to illustrate various modes of practice. From this paper, it becomes clear that e-government needs to and will be part of our life in the near future.