Querying and Mining Uncertain Data Streams

Nonfiction, Computers, Database Management, General Computing
Cover of the book Querying and Mining Uncertain Data Streams by Cheqing Jin, Aoying Zhou, World Scientific Publishing Company
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Author: Cheqing Jin, Aoying Zhou ISBN: 9789813142923
Publisher: World Scientific Publishing Company Publication: May 24, 2016
Imprint: WSPC Language: English
Author: Cheqing Jin, Aoying Zhou
ISBN: 9789813142923
Publisher: World Scientific Publishing Company
Publication: May 24, 2016
Imprint: WSPC
Language: English

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
  • Top-k Queries Over the Sliding-window Model
  • ER-Topk 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
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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:

Readership: Students and Professionals involved in data mining, big data, and data gathering.
Key Features:

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