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- W2980810158 abstract "The join operations, including both equi and non-equi joins, are essential to the complex data analytics in the big data era. However, they are not inherently supported by existing DSPEs ( D istributed S tream P rocessing E ngines). The state-of-the-art join solutions on DSPEs rely on either complicated routing strategies or resource-inefficient processing structures, which are susceptible to dynamic workload, especially when the DSPEs face various join predicate operations and skewed data distribution. In this paper, we propose a new cost-effective stream join framework, named A-DSP ( A daptive D imensional S pace P rocessing), which enhances the adaptability of real-time join model and minimizes the resource used over the dynamic workloads. Our proposal includes: 1) a join model generation algorithm devised to adaptively switch between different join schemes so as to minimize the number of processing task required; 2) a load-balancing mechanism which maximizes the processing throughput; and 3) a lightweight algorithm designed for cutting down unnecessary migration cost. Extensive experiments are conducted to compare our proposal against state-of-the-art solutions on both benchmark and real-world workloads. The experimental results verify the effectiveness of our method, especially on reducing the operational cost under pay-as-you-go pricing scheme." @default.
- W2980810158 created "2019-10-25" @default.
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- W2980810158 date "2020-01-01" @default.
- W2980810158 modified "2023-09-27" @default.
- W2980810158 title "A-DSP: An Adaptive Join Algorithm for Dynamic Data Stream on Cloud System" @default.
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- W2980810158 doi "https://doi.org/10.1109/tkde.2019.2947055" @default.
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