New Estimation Methods of Count-Min Sketch

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	Similar Articles Articles by Hongsong Li Articles by Houkuan Huang

15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)

Tokyo, Japan

April 03-April 04

ISBN: 0-7695-2390-0

	ASCII Text	x
	Hongsong Li, Houkuan Huang, "New Estimation Methods of Count-Min Sketch," Research Issues in Data Engineering, International Workshop on, pp. 73-80, 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05), 2005.

	BibTex	x
	@article{ 10.1109/RIDE.2005.12, author = {Hongsong Li and Houkuan Huang}, title = {New Estimation Methods of Count-Min Sketch}, journal ={Research Issues in Data Engineering, International Workshop on}, volume = {0}, year = {2005}, issn = {1097-8585}, pages = {73-80}, doi = {http://doi.ieeecomputersociety.org/10.1109/RIDE.2005.12}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }

	RefWorks Procite/RefMan/Endnote	x
	TY - CONF JO - Research Issues in Data Engineering, International Workshop on TI - New Estimation Methods of Count-Min Sketch SN - 1097-8585 SP73 EP80 A1 - Hongsong Li, A1 - Houkuan Huang, PY - 2005 KW - null VL - 0 JA - Research Issues in Data Engineering, International Workshop on ER -

Hongsong Li, Beijing Jiaotong University

Houkuan Huang, Beijing Jiaotong University

DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/RIDE.2005.12

Count-Min sketch is an efficient approximate query tool for data stream. In this paper, we address how to further improve its point query performance. Firstly, we modify the estimation method under cash register model, Our method will relieve error propagation. Secondly, we find better method under turnstile model and prove that our method is more efficient than that Count-Min sketch. These conclusions are well supported by experimental results.

Citation:

Hongsong Li, Houkuan Huang, "New Estimation Methods of Count-Min Sketch," ride, pp.73-80, 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05), 2005

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