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  • 标题:Data Analysis using Adaptive Join Operators for Result Rate Optimization on Streaming Inputs
  • 本地全文:下载
  • 作者:Dr. G. Srinivas ; Dr. V. Suryanarayana ; Sri. B. Mouleswara Rao
  • 期刊名称:International Journal of Computer Science & Technology
  • 印刷版ISSN:2229-4333
  • 电子版ISSN:0976-8491
  • 出版年度:2012
  • 卷号:3
  • 期号:2
  • 页码:774-777
  • 语种:English
  • 出版社:Ayushmaan Technologies
  • 摘要:Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments. Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays. In this paper, we are improving the present techniques by introducing data analysis over heterogeneous network environments by keeping the same result rate optimization on streaming inputs. We follow the (DINER) Double Index NEsted-loops Reactive join a new adaptive twoway join algorithm for result rate maximization. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results, outperforming existing techniques for adaptive multiway join.
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