Finding Frequent Items in Data Streams-Reference-Cited by-同舟云学术

Finding Frequent Items in Data Streams

Published:2002 Issue: Volume: Page:693-703
ISSN:0302-9743
Container-title:Automata, Languages and Programming
language:
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Author:

Charikar Moses,Chen Kevin,Farach-Colton Martin

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/3-540-45465-9_59

Reference12 articles.

1. Dimitris Achlioptas. Database-friendly random projections. In Proc. 20th ACM Symposium on Principles of Database Systems, pages 274–281, 2001.

2. Noga Alon, Yossi Matias, and Mario Szegedy. The space complexity of approximating the frequency moments. Journal of Computer and System Sciences, 58(1):137–147, 1999.

3. Joan Feigenbaum, Sampath Kannan, Martin Strauss, and Mahesh Viswanathan. An approximate l 1-difference algotihm for massive data streams. In Proc. 40th IEEE Symposium on Foundations of Computer Science, pages 501–511, 1999.

4. Joan Feigenbaum, Sampath Kannan, Martin Strauss, and Mahesh Viswanathan. Testing and spot-checking of data streams. In Proc. 11th ACM-SIAM Symposium on Discrete Algorithms, pages 165–174, 2000.

5. Min Fang, Narayanan Shivakumar, Hector Garcia-Molina, Rajeev Motwani, and Jeffrey Ullman. Computing iceberg queries efficiently. In Proc. 22nd International Conference on Very Large Data Bases, pages 307–317, 1996.

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