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Parallel XPath query based on cost optimization

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Abstract

The performance of XPath query is the key factor to the capacity of XML processing. It is an important way to improve the performance of XPath by making full use of multi-threaded computing resources for parallel processing. However, in the process of XPath parallelization, load imbalance and thread inefficiency often lead to the decline of parallel performance. In this paper, we propose a cost optimization-based parallel XPath query method named coPXQ. This method improves the parallel processing effect of navigational XPath query through a series of optimization measures. The main measures include as follows: first, by optimizing the storage of XML node relation index, both storage and access efficiency of the index are improved. Secondly, load balancing is realized by a new cost estimation method according to the number of XML node relations to optimize parallel relation index creation and parallel primitive execution. Thirdly, the strategy of determining the number of worker threads based on parallel effectiveness estimation is utilized to ensure the effective use of threads in query. Compared with the existing typical methods, the experimental results show that our method can obtain better parallel performance.

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Acknowledgements

This research was supported by the Natural Science Foundation of Fujian Province of China (2018J01538, 2020J01697), the Science Foundation of Jimei University (ZQ2014003), and Open Fund of Digital Fujian Big Data Modeling and Intelligent Computing Institute.

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Authors and Affiliations

  1. Computer Engineering College, Jimei University, Xiamen, China

    Rongxin Chen, Zhijin Wang, Shutong Xie & Zongyue Wang

  2. Digital Fujian Big Data Modeling and Intelligent Computing Institute, Xiamen, China

    Rongxin Chen

  3. College of Computer Science, Beijing University of Technology, Beijing, China

    Hang Su

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  1. Rongxin Chen
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  2. Zhijin Wang
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Correspondence to Rongxin Chen.

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Chen, R., Wang, Z., Su, H. et al. Parallel XPath query based on cost optimization. J Supercomput 78, 5420–5449 (2022). https://doi.org/10.1007/s11227-021-04074-y

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