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Managing and Mining Uncertain Data

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Managing and Mining Uncertain Data Cover

 

Synopses & Reviews

Publisher Comments:

Managing and Mining Uncertain Data contains surveys by well known researchers in the field of uncertain databases. The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field. The idea is to make it accessible not only to researchers, but also to application-driven practitioners for solving real problems. Given the lack of structurally organized information on the new and emerging area of uncertain data, this book provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level database students in computer science and engineering. Editor Biography Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 120 papers in major conferences and journals in the database and data mining field. He has applied for or been granted over 65 US and International patents, and has thrice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 17 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Corporate award for Environmental Excellence in 2003. He is a recipient of the IBM Outstanding Innovation Award in 2008 for his scientific contributions to privacy technology, and a recipient of the IBM Research Division award for his contributions to stream mining for the System S project. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and program vice-chairs for the SIAM Conference on Data Mining 2007, ICDM Conference 2007, and the WWW Conference, 2009. He served as an associate editor of the IEEE Transactions on Data Engineering from 2004 to 2008. He is an associate editor of the ACM SIGKDD Explorations and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE and a life-member of the ACM.

Synopsis:

This collection of surveys presents the most recent models, algorithms and applications in mining uncertain data. Organized to make it more accessible to applications-driven practitioners, it includes case studies based on real-world examples.

Synopsis:

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.

Table of Contents

Preface.- Introduction.- Uncertain Data Management: Introduction.- Models for Incomplete and Probabilistic Information.- Probabilistic and Relational Models with Uncertain Data.- ULDB and the Trio System.- Indexing Uncertain Data.- Probabilistic Graphical Models.- Query Evaluation for Uncertain Data.- Clustering Uncertain Data.- Classification of Uncertain Data.- Sketching Probabilistic Data.- Uncertain Spatio-temporal Applications.- Uncertain Representations and Applications in Sensor Networks.- OLAP over Uncertain data.- Index.

Product Details

ISBN:
9781441935175
Author:
Aggarwal, Charu C.
Publisher:
Springer
Subject:
Aggarwal
Subject:
Data
Subject:
Managing
Subject:
Mining
Subject:
Uncertain
Subject:
Data Mining and Knowledge Discovery
Subject:
Database management
Subject:
Artificial Intelligence (incl. Robotics)
Subject:
Information storage and retrieval.
Subject:
Systems and Data Security
Subject:
Information Systems Applications (incl.Internet)
Subject:
Computers-Reference - General
Copyright:
Edition Description:
Softcover reprint of hardcover 1st ed. 2009
Series:
Advances in Database Systems
Series Volume:
35
Publication Date:
20101206
Binding:
TRADE PAPER
Language:
English
Pages:
516
Dimensions:
235 x 155 mm 769 gr

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Computers Reference » General
Computers and Internet » Internet » Information

Managing and Mining Uncertain Data New Trade Paper
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Product details 516 pages Springer - English 9781441935175 Reviews:
"Synopsis" by , This collection of surveys presents the most recent models, algorithms and applications in mining uncertain data. Organized to make it more accessible to applications-driven practitioners, it includes case studies based on real-world examples.
"Synopsis" by , 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.
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