Ranking Queries on Uncertain Data

  • Ming Hua
  • Jian Pei

Part of the Advances in Database Systems book series (ADBS, volume 42)

Table of contents

  1. Front Matter
    Pages 1-13
  2. Ming Hua, Jian Pei
    Pages 1-7
  3. Ming Hua, Jian Pei
    Pages 9-32
  4. Ming Hua, Jian Pei
    Pages 33-50
  5. Ming Hua, Jian Pei
    Pages 51-87
  6. Ming Hua, Jian Pei
    Pages 89-128
  7. Ming Hua, Jian Pei
    Pages 129-150
  8. Ming Hua, Jian Pei
    Pages 151-184
  9. Ming Hua, Jian Pei
    Pages 185-206
  10. Ming Hua, Jian Pei
    Pages 207-214
  11. Back Matter
    Pages 226-232

About this book

Introduction

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.

Keywords

Uncertain data data sets database model empirical evaluation probabilistic data query processing ranking queries record linkage uncertain stream data

Authors and affiliations

  • Ming Hua
    • 1
  • Jian Pei
    • 2
  1. 1.Facebook Inc.Palo AltoUSA
  2. 2.School of Computing ScienceSimon Fraser UniversityBurnabyCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4419-9380-9
  • Copyright Information Springer Science+Business Media, LLC 2011
  • Publisher Name Springer, New York, NY
  • eBook Packages Computer Science
  • Print ISBN 978-1-4419-9379-3
  • Online ISBN 978-1-4419-9380-9
  • Series Print ISSN 1386-2944
  • About this book