Ranking Queries on Uncertain Data

By Ming Hua , Jian Pei

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With ‘uncertain’ data present in numerous important applications such as market analysis and environmental surveillance, this text surveys the fundamental principles, key challenges, applications and evaluation algorithms of ranking queries on uncertain data.

Full Description

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  • ISBN13: 978-1-4419-9379-3
  • 240 Pages
  • User Level: Science
  • Publication Date: March 28, 2011
  • Available eBook Formats: PDF

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Full Description
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.
Table of Contents

Table of Contents

  1. Introduction.
  2. Probabilistic Ranking Queries on Uncertain Data.
  3. Related Work.
  4. Top
  5. k Typicality Queries on Uncertain Data.
  6. Probabilistic Ranking Queries on Uncertain Data.
  7. Continuous Ranking Queries on Uncertain Streams.
  8. Ranking Queries on Probabilistic Linkages.
  9. Probabilistic Path Queries on Road Networks.
  10. Conclusions.
  11. References
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