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OverviewUncertain data is inherent in many important applications, such as environmental surveillance, market analysis, and quantitative economics research. Due to the importance of those applications and rapidly increasing amounts of uncertain data collected and accumulated, analyzing large collections of uncertain data has become an important task. Ranking queries (also known as top-k queries) are often natural and useful in analyzing uncertain data. Ranking Queries on Uncertain Data discusses the motivations/applications, challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data. Theoretical and algorithmic results of ranking queries on uncertain data are presented in the last section of this book. Ranking Queries on Uncertain Data is the first book to systematically discuss the problem of ranking queries on uncertain data. Full Product DetailsAuthor: Ming Hua , Jian PeiPublisher: Springer-Verlag New York Inc. Imprint: Springer-Verlag New York Inc. Edition: 2011 ed. Volume: 42 Dimensions: Width: 15.50cm , Height: 1.40cm , Length: 23.50cm Weight: 0.517kg ISBN: 9781441993793ISBN 10: 1441993797 Pages: 224 Publication Date: 12 April 2011 Audience: Professional and scholarly , Professional & Vocational Format: Hardback Publisher's Status: Active Availability: In Print ![]() This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |