"Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possib...

Full description

Bibliographic Details

Main Author:	Jin, Cheqing (Author)
Format:	Electronic eBook
Language:	English
Published:	Singapore World Scientific Publishing Co. Pte. Ltd. c2016
Series:	East China Normal University scientific reports volume 3
Subjects:	Data mining Querying (Computer science) Uncertainty (Information theory)
Online Access:	FHN01 Volltext
Summary:	"Data uncertainty widely exists in many applications, and an uncertain data stream is a series of uncertain tuples that arrive rapidly. However, traditional techniques for deterministic data streams cannot be applied to deal with data uncertainty directly due to the exponential growth of possible solution space. "This book provides a comprehensive overview of the authors' work on querying and mining uncertain data streams. Its contents include some important discoveries dealing with typical topics such as top-k query, ER-Topk query, rarity estimation, set similarity, and clustering. Querying and Mining Uncertain Data Streams is written for professionals, researchers, and graduate students in data mining and its various related fields. Contents: Introduction; Queries Over the Sliding-window Model ; Query Over the Landmark Model ; Rarity Estimation ; Set similarity; Clustering; Conclusion. Readership: Students and Professionals involved in data mining, big data, and data gathering. Key Features: The first book on uncertain data stream management. There exist significant contributions on typical topics."--
Item Description:	Title from PDF file title page (viewed June 16, 2016)
Physical Description:	1 online resource (xvi, 148 p.)
ISBN:	9789813142916