Generic group algorithms solve computational problems defined over algebraic groups without exploiting properties of a particular representation of group elements. This is modeled by treating the group as a black-box. The fact that a computational problem cannot be solved by a reasonably restricted class of algorithms may be seen as support towards the conjecture that the problem is also hard in the classical Turing machine model. Moreover, a lower complexity bound for certain algorithms is a helpful insight for the search for cryptanalytic algorithms.

Knowledge discovery in ubiquitous environments is an emerging area of research at the intersection of the two major challenges of highly distributed and mobile systems and advanced knowledge discovery systems. It aims to provide a unifying framework for systematically investigating the mutual dependencies of otherwise quite unrelated technologies employed in building next-generation intelligent systems: machine learning, data mining, sensor networks, grids, peer-to-peer networks, data stream mining, activity recognition, Web 2.0, privacy, user modelling and others. This state-of-the-art survey is the outcome of a large number of workshops, summer schools, tutorials and dissemination events organized by KDubiq (Knowledge Discovery in Ubiquitous Environments), a networking project funded by the European Commission to bring together researchers and practitioners of this emerging community. It provides in its first part a conceptual foundation for the new field of ubiquitous knowledge discovery - highlighting challenges and problems, and proposing future directions in the area of 'smart', 'adaptive', and 'intelligent' learning. The second part of this volume contains selected approaches to ubiquitous knowledge discovery and treats specific aspects in detail. The contributions have been carefully selected to provide illustrations and in-depth discussions for some of the major findings of Part I.

Sentiment analysis deals with extracting information about opinions, sentiments, and even emotions conveyed by writers towards topics of interest. Medical sentiment analysis refers to the identification and analysis of sentiments or emotions expressed in free-textual documents with a scope on healthcare and medicine. This fascinating problem offers numerous application areas in the domain of medicine, but also research challenges. The book provides a comprehensive introduction to the topic. The primary purpose is to provide the necessary background on medical sentiment analysis, ranging from a description of the notions of medical sentiment to use cases that have been considered already and application areas of relevance. Medical sentiment analysis uses natural language processing (NLP), text analysis and machine learning to realise the process of extracting and classifying statements regarding expressed opinion and sentiment. The book offers a comprehensive overview on existing methods of sentiment analysis applied to healthcare resources or health-related documents. It concludes with open research avenues providing researchers indications which topics still have to be developed in more depth.

This volume contains 18 invited papers by members and guests of the former Sonderforschungsbereich in Bonn (SFB 72) who, over the years, collaborated on the research group "Solution of PDE's and Calculus of Variations". The emphasis is on existence and regularity results, on special equations of mathematical physics and on scattering theory.

This book addresses the issue of uniqueness of a solution to a problem – a very important topic in science and technology, particularly in the field of partial differential equations, where uniqueness guarantees that certain partial differential equations are sufficient to model a given phenomenon.

This volume is dedicated to topology and will contribute to stimulating research in this basic area of mathematics.

This volume is a critical book that provides different methodologies that combine the potential data (including Big Data) analysis with GIS applications. It gives readers a comprehensive overview of the current state-of-the-art methods of spatial analysis, focusing both on the new philosophical and theoretical foundations for spatial analysis and on a flexible framework for analysis in the real world, for problems such as complexity and uncertainty.

This book presents aspects of predictive maintenance in industries and shows the possibility of reaching the creation of prognostic maintenance. It offers techniques for managing maintenance processes theoretically and practically as examples of possible industrial implementations.