As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs."

This book constitutes the refereed proceedings of the 6th International Workshop on Next Generation Information Technologies and Systems, NGITS 2006, held in Kibbutz Shefayim, Israel, July 2006. The book presents 28 revised full papers and four revised short papers together with three invited papers. Topical sections include information integration, next generation applications, information systems development, security and privacy, semi-structured data, frameworks, models and taxonomies, simulation and incremental computing, and more.

This book constitutes the refereed proceedings of the 5th International Conference on Flexible Query Answering Systems, FQAS 2002, held in Copenhagen, Denmark, in October 2002. The 29 revised full papers presented were carefully reviewed and selected for inclusion in the proceedings. Among the fields covered are database management, information retrieval, domain modeling, knowledge representation and ontologies, knowledge discovery and data mining, artificial intelligence, classical and nonclassical logics, computational linguistics, natural language processing, multimedia information systems, and human-computer interaction.

As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view points of information systems experts on the needs that challenge the uncer tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs."