Consistent View Based Management Of Variability In Space And Time

Developing variable systems faces many challenges. Dependencies between interrelated artifacts within a product variant, such as code or diagrams, across product variants and across their revisions quickly lead to inconsistencies during evolution. This work provides a unification of common concepts and operations for variability management, identifies variability-related inconsistencies and presents an approach for view-based consistency preservation of variable systems.

In this work, the authors analysed the co-dependency between models and analyses, particularly the structure and interdependence of artefacts and the feature-based decomposition and composition of model-based analyses. Their goal is to improve the maintainability of model-based analyses. They have investigated the co-dependency of Domain-specific Modelling Languages (DSMLs) and model-based analyses regarding evolvability, understandability, and reusability.

Although tremendous progress has been made in Artificial Intelligence (AI), it entails new challenges. The growing complexity of learning tasks requires more complex AI components, which increasingly exhibit unreliable behaviour. In this book, we present a model-driven approach to model architectural safeguards for AI components and analyse their effect on the overall system reliability.

This cumulative habilitation thesis, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.

Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.

This book constitutes the refereed proceedings of the First Asian Semantic Web Conference, ASWC 2006, held in Beijing, China, in September 2006. The 36 revised full papers and 36 revised short papers presented together with three invited contributions were carefully reviewed and selected from 208 full paper submissions. The papers are organized in topical sections.

'Systemic management' describes a holistic, objective and universally applicable form of management, providing a framework for addressing environmental challenges such as global warming, emergent diseases, deforestation, overpopulation, the extinction crisis, pollution, over-fishing, and habitat destruction. Its goals are the consistently sustainable relationships between humans and ecosystems, between humans and other species, and between humans and the biosphere. This book presents a convincing argument that these goals, and the means to achieve them, can be inferred from empirical information. It describes how comparisons between humans and other species reveal patterns that can serve to guide management toward true sustainability i.e. ways that are empirically observed to work in natural systems. This objective approach has rarely been possible in conventional management because sustainability is invariably undermined by conflicting human values. 'Systemic management' is presented as a specialized process of pattern-based decision-making that avoids the inconsistency, subjectivity and error in current management practice. It clearly demonstrates how mimicking nature's empirical examples of sustainability can circumvent anthropocentric tendencies to overuse/misuse human values in management, and illustrates the science best suited for achieving sustainability through examples of research that address specific management questions.