Scalability is one of the main problems practitioners have to cope with when grasping a real-world application in data management or information analysis. The size of databases and data warehouses, associated with incompleteness of information and missing values has been a major difficulty from theearly beginning of their studies. Modern digital devices, Internet possibilities, and distributed networksare among the most powerful means of storing, retrieving, and sharing information. The amounts of documents and data available for the users are continuously increasing, whatever their nature may be: text, video, music, images, multimedia, Web. The ways to access these documents and data are also diverse: exchanges within communities, social networks and peer to peer communications have increased the complexity of transfers from data repositories to users. To increase the efficiency of existing algorithms is a necessity. Dimension reduction or dynamic treatment of data avoiding their storage is for instance a solution to large scale learning systems. Moreover, alternative approaches to classic information retrieval, knowledge discovery and data analysis need to be created, in order to cope with the complexity of the problem to solve, due to the size, the heterogeneity, the incompleteness of data and their access paths. Thinking differently is also a necessity since classic statistics or machine learning methods have their limits. System science provides interesting paradigms for the handling of complex systems, always taking the user into account, in a holistic involvement of all components of the system. Active learning involving the user is for example a solution to the difficulty of using supervised learning in huge training sets. Another lesson from systems science is the exploitation of synergies between components of the system, and this capacity is well understood in the complementarity between medias, for instance between text and image.Fuzzy knowledge representation and logic are among the efficient tools for the management of complexsystems, since they bring solutions to the incompleteness, inaccuracy and uncertainty, inherent to largescale and heterogeneous information reservoirs, taking into account synthetic descriptions of isolated elements and reducing individual treatments. Providing an interface between numerical data representations by computers and symbolic representations well understood by humans, fuzzy logic fills in the gap between technological needs and usability requirements. Concepts such as fuzzy categories, fuzzy quantifiers, fuzzy prototypes, fuzzy aggregation methods, fuzzy learning algorithms, fuzzy databases, and fuzzy graphs have proved their utility in the construction of scalable algorithms.The present book is certainly of particular interest for the diversity of addressed topics, covering a large spectrum in scalability management. Anne Laurent and Marie-Jeanne Lesot are experts in theoretical and methodological study of fuzzy techniques, and they have moreover coped with various real world large-scale problems. The group of experts they have gathered to prepare this volume is unquestionably qualified to provide solutions to researchers and practitioners in search of efficient algorithms and models for complex and large dataset management and analysis.
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