Managing and Mining Uncertain Data

herausgegeben von: Charu C. Aggarwal

Verlag: Springer US

Buchreihe : Advances in Database Systems

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

Inhaltsverzeichnis

Backmatter

Titel: Managing and Mining Uncertain Data
herausgegeben von: Charu C. Aggarwal
Verlag: Springer US
Electronic ISBN: 978-0-387-09690-2
Print ISBN: 978-0-387-09689-6
DOI: https://doi.org/10.1007/978-0-387-09690-2

Springer Professional

Managing and Mining Uncertain Data

Über dieses Buch

Inhaltsverzeichnis

Frontmatter

1. An Introduction to Uncertain Data Algorithms and Applications

2. Models for Incomplete and Probabilistic Information

3. Relational Models and Algebra for Uncertain Data

4. Graphical Models for Uncertain Data

5. Trio A System for Data Uncertainty and Lineage

6. MayBMS A System for Managing Large Probabilistic Databases

7. Uncertainty in Data Integration

8. Sketching Aggregates over Probabilistic Streams

9. Probabilistic Join Queries in Uncertain Databases

10. Indexing Uncertain Data

11. Querying Uncertain Spatiotemporal Data

12. Probabilistic XML

13. On Clustering Algorithms for Uncertain Data

14. On Applications of Density Transforms for Uncertain Data Mining

15. Frequent Pattern Mining Algorithms with Uncertain Data

16. Probabilistic Querying and Mining of Biological Images

Backmatter