Finding frequent items in data streams

Author:

Charikar Moses,Chen Kevin,Farach-Colton Martin

Publisher

Elsevier BV

Subject

General Computer Science,Theoretical Computer Science

Reference17 articles.

1. D. Achlioptas, Database-friendly random projections, in: Proc. 20th ACM Symp. on Principles of Database Systems, 2001, pp. 274–281.

2. The space complexity of approximating the frequency moments;Alon;J. Comput. System Sci.,1999

3. M.E. Crovella, M.S. Taqqu, A. Bestavros, Heavy-tailed probability distributions in the world wide web, in: Adler, Feldman, Taqqu (Eds.), A Practical Guide to Heavy Tails, Birkhäuser, Basel, 1998.

4. M. Fang, N. Shivakumar, H. Garcia-Molina, R. Motwani, J. Ullman, Computing iceberg queries efficiently, in: Proc. 22nd Internat. Conf. on Very Large Data Bases, 1996, pp. 307–317.

5. J. Feigenbaum, S. Kannan, M. Strauss, M. Viswanathan, An approximate l1-difference algorithm for massive data streams, in: Proc. 40th IEEE Symp. on Foundations of Computer Science, 1999, pp. 501–511.

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