最小计数草图的新估计方法

15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05) Pub Date : 2005-04-03 DOI:10.1109/RIDE.2005.12

Hongsong Li, Houkuan Huang

{"title":"最小计数草图的新估计方法","authors":"Hongsong Li, Houkuan Huang","doi":"10.1109/RIDE.2005.12","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":404914,"journal":{"name":"15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2005-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"New estimation methods of Count-Min sketch\",\"authors\":\"Hongsong Li, Houkuan Huang\",\"doi\":\"10.1109/RIDE.2005.12\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":404914,\"journal\":{\"name\":\"15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)\",\"volume\":\"45 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/RIDE.2005.12\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/RIDE.2005.12","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 3

摘要

最小计数草图是一种高效的数据流近似查询工具。在本文中，我们讨论了如何进一步提高其点查询性能。首先，对收款机模型下的估计方法进行了改进。我们的方法可以减少误差的传播。其次，我们在旋转门模型下找到了更好的方法，并证明了我们的方法比Count-Min草图更有效。实验结果很好地支持了这些结论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

New estimation methods of Count-Min sketch

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.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

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

自引率

0.00%

发文量