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Linked-Tree: An Aggregate Query Algorithm Based on Sliding Window over Data Stream

Linked-Tree: An Aggregate Query Algorithm Based on Sliding Window over Data Stream
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摘要 How to process aggregate queries over data streams efficiently and effectively have been becoming hot re search topics in both academic community and industrial community. Aiming at the issues, a novel Linked-tree algorithm based on sliding window is proposed in this paper. Due to the proposal of concept area, the Linked-tree algorithm reuses many primary results in last window and then avoids lots of unnecessary repeated comparison operations between two successive windows. As a result, execution efficiency of MAX query is improved dramatically. In addition, since the size of memory is relevant to the number of areas but irrelevant to the size of sliding window, memory is economized greatly. The extensive experimental results show that the performance of Linked-tree algorithm has significant improvement gains over the traditional SC (Simple Compared) algorithm and Ranked-tree algorithm. How to process aggregate queries over data streams efficiently and effectively have been becoming hot re search topics in both academic community and industrial community. Aiming at the issues, a novel Linked-tree algorithm based on sliding window is proposed in this paper. Due to the proposal of concept area, the Linked-tree algorithm reuses many primary results in last window and then avoids lots of unnecessary repeated comparison operations between two successive windows. As a result, execution efficiency of MAX query is improved dramatically. In addition, since the size of memory is relevant to the number of areas but irrelevant to the size of sliding window, memory is economized greatly. The extensive experimental results show that the performance of Linked-tree algorithm has significant improvement gains over the traditional SC (Simple Compared) algorithm and Ranked-tree algorithm.
出处 《Wuhan University Journal of Natural Sciences》 CAS 2006年第5期1114-1119,共6页 武汉大学学报(自然科学英文版)
基金 Supported by the National Natural Science Foun-dation of China (60573089) the National 985 Project Fundation(985-2-DB-Y01)
关键词 data streams sliding window aggregate query area HOP data streams sliding window aggregate query area hop
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参考文献10

  • 1Babcock B,,Babu S,Datar M, et al.Models and Issues in Data Stream Systems [C]//[].Proc of the st ACM Symposium on Principles of Database Systems.2002
  • 2Alon N,Matias Y,Szegedy M.The Space Complexity of Approximating the Frequency Moments [ C]//[].Proc of the th ACM Symposium on Theory of Computing.1996
  • 3Flajolet,P,Martin,G. Probabilistic Counting . 1983
  • 4Acharya S,Gibbons P B,Poosala V.Congressional Samples for Approximate Answering of Group-by Queries [C]/[]./Proc of the ACM SIGMOD Intl Conf on Management of Data.2000
  • 5Acharya S,Gibbons P B,Poosala V, et al.Join Synopses for Approximate Query Answering [ C]//[].Proc of the ACM SIGMOD Intl Conf on Management of Data.1999
  • 6Chaudhuri S,Motwani R,Narasayya V.On Random Sampling over Joins[].// Proc of the ACM SIGMOD Intl Conf On Management of Data.1999
  • 7Ioannidis Y E,,Poosala V.Histogram-Based Approximation of Set-Valued Query-Answers [ C]//[].Proc of the th Intl Conf on Very Large Data Bases.1999
  • 8Chakrabarti K K,Garofalakis M N,Rastogi R, et al.Approximate Query Processing Using Wavelets [ C]//[].Proc of the th Int’l Conf on Very Large Data Bases.2000
  • 9Guha S S,,Kim Chulyun,Shim Kyuseok.XWAVE : Approximate Extended Wavelets for Streaming Data[].Proc of the th Intl Conf on Very Large Data Bases.2004
  • 10Gehrke J,,Korn F,Srivastava D.On Computing Correlated Aggregates over Continual Data Streams [ C]//[].Pro of the ACM SIGMOD Int Conf on Management of Data.2001

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