Ranking Queries on Uncertain Data
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Beschreibung
Uncertain 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.
Presents challenging problems, the fundamental principles, and the evaluation algorithms of ranking queries on uncertain data Includes efficient and scalable query evaluation algorithms for the ranking queries Covers a comprehensive empirical evaluation of the queries The first book to systematically discuss the problem of ranking queries on uncertain data Includes supplementary material: sn.pub/extras
Autor*in
Ming Hua
Themen in »Ranking Queries on Uncertain Data«
Uncertain data data sets database model empirical evaluation probabilistic data query processing ranking queries record linkage uncertain stream data