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Introduction.- Probabilistic Ranking Queries on Uncertain Data.- Related Work.- Top-k Typicality Queries on Uncertain Data.- Probabilistic Ranking Queries on Uncertain Data.- Continuous Ranking Queries on Uncertain Streams.- Ranking Queries on Probabilistic Linkages.- Probabilistic Path Queries on Road Networks.- Conclusions.- References

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.

Format
PDF-ebook
Language
english 
Age
02-99
Pages
224
File Size
2.0 MB
retrievable
24 months after purchase
Copy Protection
Adobe DRM
Publisher
Springer New York
place of publication
NY/US
Edition
Published
Product No.
DG2150608

'Ranking Queries on Uncertain Data' by Ming Hua & Jian Pei is a digital PDF ebook for direct download to PC, Mac, Notebook, Tablet, iPad, iPhone, Smartphone, eReader - but not for Kindle. A DRM capable reader equipment is required.

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