By JIN CHEQING & ZHOU AOYING,Aoying Zhou

Data uncertainty largely exists in lots of functions, and an doubtful information flow is a chain of doubtful tuples that arrive speedily. even if, conventional recommendations for deterministic facts streams can't be utilized to accommodate facts uncertainty without delay as a result of the exponential progress of attainable answer space.

This e-book offers a accomplished assessment of the authors' paintings on querying and mining doubtful info streams. Its contents comprise a few vital discoveries facing ordinary subject matters akin to top-k question, ER-Topk question, rarity estimation, set similarity, and clustering.

Querying and Mining doubtful facts Streams is written for pros, researchers, and graduate scholars in information mining and its numerous similar fields.

Contents:

  • Introduction
  • Top-k Queries Over the Sliding-window Model
  • ER-Topk question Over the Landmark Model
  • Rarity Estimation
  • Set Similarity
  • Clustering
  • Conclusion

Readership: scholars and pros interested by information mining, vast information, and information gathering.
Key Features:

  • The first publication on doubtful info circulation management
  • There exist major contributions on regular topics

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QUERYING AND MINING UNCERTAIN DATA STREAMS: 3 (EAST CHINA NORMAL UNIVERSITY SCIENTIFIC REPORTS)

Information uncertainty commonly exists in lots of purposes, and an doubtful facts movement is a chain of doubtful tuples that arrive speedily. despite the fact that, conventional suggestions for deterministic information streams can't be utilized to house info uncertainty at once as a result of the exponential progress of attainable resolution area.

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