This book constitutes the refereed joint proceedings of five international workshops held in conjunction with the 24th International Conference on Conceptual Modeling, ER 2005, in Klagenfurt, Austria, in October 2005. The 40 revised full papers presented together with the abstracts of seven tutorials were carefully reviewed and selected from 102 submissions. The papers are organized in topical sections on best practices of UML, experience reports and new applications, model evaluation and requirements modeling, metamodeling and model driven development, positions in engineering agent oriented systems, agent oriented methodologies and conceptual modeling, agent communication and coordination, geographic information systems, spatial and spatio-temporal data representation, spatial relations, spatial queries, analysis and data mining, data modeling and visualisation, conceptual modeling approaches for e-business, information system models quality, and quality driven processes.

Conceptual modeling has always been one of the main issues in information systems engineering as it aims to describe the general knowledge of the system at an abstract level that facilitates user understanding and software development. This collection of selected papers provides a comprehensive and extremely readable overview of what conceptual modeling is and perspectives on making it more and more relevant in our society. It covers topics like modeling the human genome, blockchain technology, model-driven software development, data integration, and wiki-like repositories and demonstrates the general applicability of conceptual modeling to various problems in diverse domains. Overall, this book is a source of inspiration for everybody in academia working on the vision of creating a strong, fruitful and creative community of conceptual modelers. With this book the editors and authors want to honor Prof. Antoni Olivé for his enormous and ongoing contributions to the conceptual modeling discipline. It was presented to him on the occasion of his keynote at ER 2017 in Valencia, a conference that he has contributed to and supported for over 20 years. Thank you very much to Antoni for so many years of cooperation and friendship.

The growing demand for systems of ever-increasing complexity and precision has stimulated the need for higher level concepts, tools, and techniques in every area of Computer Science. Some of these areas, in particular Artificial Intelligence, Databases, and Programming Lan guages, are attempting to meet this demand by defining a new, more abstract level of system description. We call this new level conceptual in recognition of its basic conceptual nature. In Artificial Intelligence, the problem of designing an expert system is seen primarily as a problem of building a knowledge base that repre sents knowledge about an enterprise. Consequently, Knowledge Repre sentation is viewed as a central issue in Artificial Intelligence research. Database design methodologies developed during the last five years are almost unanimous in offering semantic data models in terms of which the designer directly and naturally models an enterprise before proceed ing to a detailed logical and physical database design. In Programming Languages, different forms of abstraction which allow implementation independent specifications of data, functions, and control have been a major research theme for a decade. To emphasize the common goals of these three research efforts, we call this new activity conceptual modelling.

When seeking to test specific hypotheses in large data sets, social and behavioral scientists often construct models. Although useful in such situations, many phenomena of interest do not occur in large samples and do not lend themselves to precise measurement. In addition, a focus on hypothesis testing can constrict the potential use of models as organizing devices for emerging patterns -- summaries of what we believe we know about the dynamics of situation. This book bridges the gap between "quantitative" and "qualitative" modelers to reconcile the need to impose rigor and to understand the influence of context. Although there are many different uses for models, there is also the realistic possibility of doing credible research without their use. A critical reexamination of the assumptions used in quantitatively-oriented models, however, suggests ways to increase their effectiveness as organizers of both quantitative and qualitative data. Students of methods in psychology, sociology, education, management, social work, and public health -- and their instructors -- are increasingly expected to become familiar with both quantitative and qualitative approaches. Unfortunately, they find few vehicles for communication regarding the implications of overlapping work between the two approaches. Using models as organizing devices for a better dialogue between assumptions and data might facilitate this communication process.

The growing demand for systems of ever-increasing complexity and precision has stimulated the need for higher level concepts, tools, and techniques in every area of Computer Science. Some of these areas, in particular Artificial Intelligence, Databases, and Programming Lan guages, are attempting to meet this demand by defining a new, more abstract level of system description. We call this new level conceptual in recognition of its basic conceptual nature. In Artificial Intelligence, the problem of designing an expert system is seen primarily as a problem of building a knowledge base that repre sents knowledge about an enterprise. Consequently, Knowledge Repre sentation is viewed as a central issue in Artificial Intelligence research. Database design methodologies developed during the last five years are almost unanimous in offering semantic data models in terms of which the designer directly and naturally models an enterprise before proceed ing to a detailed logical and physical database design. In Programming Languages, different forms of abstraction which allow implementation independent specifications of data, functions, and control have been a major research theme for a decade. To emphasize the common goals of these three research efforts, we call this new activity conceptual modelling.

Conceptual modeling represents a recent approach to creating knowledge. It has emerged in response to the computer revolution, which started in the middle of the 20th century. Computers, in the meantime, have become a major knowledge media. Conceptual modeling provides an answer to the difficulties experienced throughout the development of computer applications and aims at creating effective, reasonably priced, and sharable knowledge about using computers in business. Moreover, it has become evident that conceptual modeling has the potential to exceed the boundaries of business and computer usage. This state-of-the-art survey originates from the International Seminar on the Evolution of Conceptual Modeling, held in Dagstuhl Castle, Germany, in April 2008. The major objective of this seminar was to look into conceptual modeling from a historical perspective with a view towards the future of conceptual modeling and to achieve a better understanding of conceptual modeling issues in several different domains of discourse, going beyond individual (modeling) projects. The book contains 14 chapters. These were carefully selected during two rounds of reviewing and improvement from 26 presentations at the seminar and are preceded by a detailed preface providing general insights into the field of conceptual modeling that are not necessarily discussed in any of the chapters but nevertheless aid in conceptualizing the inner structure and coherence of the field. The chapters are grouped into the following three thematic sections: the evolution of conceptual modeling techniques; the extension of conceptual modeling to a service-oriented, peer-to-peer, or Web context; and new directions for conceptual modeling.

Bringing together an international group of researchers involved in military, business, and health modeling and simulation, Conceptual Modeling for Discrete-Event Simulation presents a comprehensive view of the current state of the art in the field. The book addresses a host of issues, including: What is a conceptual model?How is conceptual modelin