Advances in Probabilistic Databases for Uncertain Information Management [electronic resource] / edited by Zongmin Ma, Li Yan.

Contributor(s):

Series: Studies in Fuzziness and Soft Computing ; 304Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: VIII, 161 p. online resourceContent type:

text

Media type:

computer

Carrier type:

online resource

ISBN:

9783642375095

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: Printed edition:: No titleDDC classification:

519 23

LOC classification:

TA329-348
TA640-643

Online resources:

Click here to access online

Contents:

1 Research in Probabilistic Spatiotemporal Databases: The SPOT Framework -- 2 A Probabilistic Object-Oriented Database Model with Fuzzy Measures -- 3 Probabilistic XML: Models and Complexity -- 4 Uncertain Data: Representations, Query Processing, and Applications -- 5 A Theoretically-Sound Approach for OLAPing Uncertain and Imprecise Multidimensional Data Streams -- 6 Tractable Probabilistic Description Logic Programs. .

In: Springer eBooksSummary: This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.

Item type:

eBooks

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This book covers a fast-growing topic in great depth and focuses on the technologies and applications of probabilistic data management. It aims to provide a single account of current studies in probabilistic data management. The objective of the book is to provide the state of the art information to researchers, practitioners, and graduate students of information technology of intelligent information processing, and at the same time serving the information technology professional faced with non-traditional applications that make the application of conventional approaches difficult or impossible.