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.