Optimal sampling algorithms for frequency estimation in distributed data

Proceedings - IEEE INFOCOM

Volumn , Issue , 2011, Pages 1997-2005

  Huang, Zengfeng ^a   Yi, Ke ^a   Liu, Yunhao ^a   Chen, Guihai ^b  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^b SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

BIT LEVEL; BLOOM FILTERS; CARDINALITIES; COMMUNICATION COST; DISTRIBUTED DATA; DISTRIBUTED SYSTEMS; LARGE-SCALE DISTRIBUTED SYSTEM; MULTI-SETS; MULTISET; OPTIMAL SAMPLING; OPTIMALITY; PERFORMANCE IMPROVEMENTS; SAMPLING ALGORITHM; SIMULATION RESULT; STANDARD DEVIATION; UNIFORM SAMPLING;

DISTRIBUTED COMPUTER SYSTEMS; LEARNING ALGORITHMS; OPTIMIZATION;

FREQUENCY ESTIMATION;

EID: 79960884457     PISSN: 0743166X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/INFCOM.2011.5935005     Document Type: Conference Paper

References (21)

1. http://en.wikipedia.org/wiki/bloom-filter.
2. B. Aronov, S. Har-Peled, and M. Sharir. On approximate halfspace range counting and relative epsilonapproximations. In Proc. Annual Symposium on Computational Geometry, pages 327-336, 2007.
3. B. Babcock and C. Olston. Distributed top-k monitoring. In Proc. ACM SIGMOD International Conference on Management of Data, 2003.
4. P. Cao and Z. Wang. Efficient top-k query calculations in distributed networks. In Proc. ACM Symposium on Principles of Distributed Computing, 2004.
5. G. Cormode, M. Garofalakis, S. Muthukrishnan, and R. Rastogi. Holistic aggregates in a networked world: Distributed tracking of approximate quantiles. In Proc. ACM SIGMOD International Conference on Management of Data, 2005.
6. G. Cormode and M. Hadjieleftheriou. Finding frequent items in data streams. In Proc. International Conference on Very Large Data Bases, 2008.
7. G. Cormode and S. Muthukrishnan. An improved data stream summary: The count-min sketch and its applications. Journal of Algorithms, 55(1):58-75, 2005.
8. J. Dean and S. Ghemawat. MapReduce: Simplified data processing on large clusters. Communications of the ACM, 51(1), 2008.
9. R. Fagin, A. Lotem, and M. Noar. Optimal aggregation algorithms for middleware. Journal of Computer and System Sciences, 66:614-656, 2003.
10. M. Greenwald and S. Khanna. Power conserving computation of order-statistics over sensor networks. In Proc. ACM Symposium on Principles of Database Systems, 2004.
11. Y. Li, P. M. Long, and A. Srinivasan. Improved bounds on the sample complexity of learning. Journal of Computer and System Sciences, 62:516-527, 2001.
12. G. Manku and R. Motwani. Approximate frequency counts over data streams. In Proc. International Conference on Very Large Data Bases, 2002.
13. A. Metwally, D. Agrawal, and A. E. Abbadi. An integrated efficient solution for computing frequent and top-k elements in data streams. ACM Transactions on Database Systems, 2006.
14. S. Michel, P. Triantafillou, and G. Weikum. KLEE: A framework for distributed top-k query algorithms. In Proc. International Conference on Very Large Data Bases, 2005.
15. B. Patt-Shamir and A. Shafrir. Approximate distributed top-k queries. Distributed Computing, 21:1-22, 2008.
16. E. Porat. An Optimal Bloom Filter Replacement Based on Matrix Solving. Computer Science-Theory and Applications, 5675:263-273, 2009.
17. N. Shrivastava, C. Buragohain, D. Agrawal, and S. Suri. Medians and beyond: New aggregation techniques for sensor networks. In Proc. International Conference on Embedded Networked Sensor Systems, 2004.
18. V. N. Vapnik and A. Y. Chervonenkis. On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability and its Applications, 16:264-280, 1971.
19. K. Yi and Q. Zhang. Optimal tracking of distributed heavy hitters and quantiles. In Proc. ACM Symposium on Principles of Database Systems, 2009.
20. H. Zhao, A. Lall, M. Ogihara, and J. Xu. Global iceberg detection over distributed data streams. In Proc. IEEE International Conference on Data Engineering, 2010.
21. Q. Zhao, M. Ogihara, H. Wang, and J. Xu. Finding global icebergs over distributed data sets. In Proc. ACM Symposium on Principles of Database Systems, 2006.