The development of net-centric approaches for intelligence and national security applications has become a major concern in many areas such as defense, intelligence and national and international law enforcement agencies. In this volume we consider the web architectures and recent developments that make n- centric approaches for intelligence and national security possible. These include developments in information integration and recent advances in web services including the concept of the semantic web. Discovery, analysis and management of web-available data pose a number of interesting challenges for research in w- based management systems. Intelligent agents and data mining are some of the techniques that can be employed. A number of specific systems that are net-centric based in various areas of military applications, intelligence and law enforcement are presented that utilize one or more of such techniques The opening chapter overviews the concepts related to ontologies which now form much of the basis of the possibility of sharing of information in the Semantic Web. In the next chapter an overview of Web Services and examples of the use of Web Services for net-centric operations as applied to meteorological and oceanographic (MetOc) data is presented and issues related to the Navy's use of MetOc Web Services are discussed. The third chapter focuses on metadata as conceived to support the concepts of a service-oriented architecture and, in particular, as it relates to the DoD Net-Centric Data Strategy and the NCES core services.

This volume is dedicated to the memory of Professor Ashley Morris who passed away some two years ago. Ashley was a close friend of all of us, the editors of this volume, and was also a Ph.D. student of one of us. We all had a chance to not only fully appreciate, and be inspired by his contributions, which have had a considerable impact on the entire research community. Due to our personal relations with Ashley, we also had an opportunity to get familiar with his deep thinking about the areas of his expertise and interests. Ashley has been involved since the very beginning of his professional career in database research and practice. Notably, he introduced first some novel solution in database management systems that could handle imprecise and uncertain data, and flexible queries based on imprecisely specified user interests. He proposed to use for that purpose fuzzy logic as an effective and efficient tool. Later the interests of Ashley moved to ways of how to represent and manipulate more complicated databases involving spatial or temporal objects. In this research he discovered and pursued the power of Geographic Information Systems (GISs).

These two main lines of Ashley 's research interests and contributions are reflected in the composition of this volume. Basically, we collected some significant papers by well known researchers and scholars on the above mentioned topics. The particular contributions will now be briefly summarized to help the reader get a view of the topics covered and the contents of the particular contributions.

This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. Fuzzy Databases: Principles and Applications is self-contained providing background material on fuzzy sets and database theory. It is comprehensive covering all of the major approaches and models of fuzzy databases that have been developed including coverage of commercial/industrial systems and applications. Background and introductory material are provided in the first two chapters. The major approaches in fuzzy databases comprise the second part of the volume. This includes the use of similarity and proximity measures as the fuzzy techniques used to extend the relational data modeling and the use of possibility theory approaches in the relational model. Coverage includes extensions to the data model, querying approaches, functional dependencies and other topics including implementation issues, information measures, database security, alternative fuzzy data models, the IFO model, and the network data models. A number of object-oriented extensions are also discussed. The use of fuzzy data modeling in geographical information systems (GIS) and use of rough sets in rough and fuzzy rough relational data models are presented. Major emphasis has been given to applications and commercialization of fuzzy databases. Several specific industrial/commercial products and applications are described. These include approaches to developing fuzzy front-end systems andspecial-purpose systems incorporating fuzziness.

The capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and granularization. Describing the uncertainty of the boundaries of deserts and mountains clearly require different tools than those provided by probability theory. The multiplicity of modalities of uncertainty appearing in GIS requires a variety of formalisms to model these uncertainties. In light of this it is natural that fuzzy set theory has become a topic of intensive interest in many areas of geographical research and applications This volume, Fuzzy Modeling with Spatial Information for Geographic Problems, provides many stimulating examples of advances in geographical research based on approaches using fuzzy sets and related technologies.

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.

Solving pattern recognition problems involves an enormous amount of computational effort. By applying genetic algorithms - a computational method based on the way chromosomes in DNA recombine - these problems are more efficiently and more accurately solved. Genetic Algorithms for Pattern Recognition covers a broad range of applications in science and technology, describing the integration of genetic algorithms in pattern recognition and machine learning problems to build intelligent recognition systems. The articles, written by leading experts from around the world, accomplish several objectives: they provide insight into the theory of genetic algorithms; they develop pattern recognition theory in light of genetic algorithms; and they illustrate applications in artificial neural networks and fuzzy logic. The cross-sectional view of current research presented in Genetic Algorithms for Pattern Recognition makes it a unique text, ideal for graduate students and researchers.

We are facing an immense growth of digital data and information resources, both in terms of size, complexity, modalities and intrusiveness. Almost every aspect of our existence is being digitally captured. This is exemplified by the omnipresent existence of all kinds of data storage, far beyond those stored in traditional relational databases. The spectrum of data being digitally stored runs from multimedia data repositories to your purchases in most stores. Every tweet that you broadcast is captured for posterity. Needless to say this situation posses new research opportunities, challenges and problems in the ways we store, manipulate, search, and - in general - make use of such data and information. Attempts to cope with these problems have been emerging all over the world with thousands of people devoted to developing tools and techniques to deal with this new area of research. One of the prominent scholars and researchers in this field was the late Professor Ashley Morris who died suddenly and tragically at a young age. Ashley's career begun in industry, where he specialized in databases.

This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. In this introduction we will describe briefly how fuzzy databases fit into the overall design of database systems and then overview the organization of the text. FUZZY DATABASE LANDSCAPE The last five years have been witness to a revolution in the database research community. The dominant data models have changed and the consensus on what constitutes worthwhile research is in flux. Also, at this time, it is possible to gain a perspective on what has been accomplished in the area of fuzzy databases. Therefore, now is an opportune time to take stock of the past and establish a framework. A framework should assist in evaluating future research through a better understanding of the different aspects of imprecision that a database can model [ 1 l.

The development of net-centric approaches for intelligence and national security applications has become a major concern in many areas such as defense, intelligence and national and international law enforcement agencies. In this volume we consider the web architectures and recent developments that make n- centric approaches for intelligence and national security possible. These include developments in information integration and recent advances in web services including the concept of the semantic web. Discovery, analysis and management of web-available data pose a number of interesting challenges for research in w- based management systems. Intelligent agents and data mining are some of the techniques that can be employed. A number of specific systems that are net-centric based in various areas of military applications, intelligence and law enforcement are presented that utilize one or more of such techniques The opening chapter overviews the concepts related to ontologies which now form much of the basis of the possibility of sharing of information in the Semantic Web. In the next chapter an overview of Web Services and examples of the use of Web Services for net-centric operations as applied to meteorological and oceanographic (MetOc) data is presented and issues related to the Navy's use of MetOc Web Services are discussed. The third chapter focuses on metadata as conceived to support the concepts of a service-oriented architecture and, in particular, as it relates to the DoD Net-Centric Data Strategy and the NCES core services.

The capabilities of modern technology are rapidly increasing, spurred on to a large extent by the tremendous advances in communications and computing. Automated vehicles and global wireless connections are some examples of these advances. In order to take advantage of such enhanced capabilities, our need to model and manipulate our knowledge of the geophysical world, using compatible representations, is also rapidly increasing. In response to this one fundamental issue of great concern in modern geographical research is how to most effectively capture the physical world around us in systems like geographical information systems (GIS). Making this task even more challenging is the fact that uncertainty plays a pervasive role in the representation, analysis and use of geospatial information. The types of uncertainty that appear in geospatial information systems are not the just simple randomness of observation, as in weather data, but are manifested in many other forms including imprecision, incompleteness and granularization. Describing the uncertainty of the boundaries of deserts and mountains clearly require different tools than those provided by probability theory. The multiplicity of modalities of uncertainty appearing in GIS requires a variety of formalisms to model these uncertainties. In light of this it is natural that fuzzy set theory has become a topic of intensive interest in many areas of geographical research and applications This volume, Fuzzy Modeling with Spatial Information for Geographic Problems, provides many stimulating examples of advances in geographical research based on approaches using fuzzy sets and related technologies.