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A dynamic and parallel approach for repetitive prime labeling of XML with MapReduce

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Abstract

A massive amount of extensible markup language (XML) data from various areas is available on the Web. Answering structural queries against XML data is important, as it is the core of information retrieval systems for XML data. Labeling scheme has been suggested for rapid query processing of massive XML data. Interval-based, prefix-based, and prime number labeling scheme exist. Of these, the prime number labeling scheme has the advantage of query processing by arithmetic operations. Recently, the repetitive prime number labeling scheme was proposed; this scheme produces a smaller label size than conventional prime number labeling using prime numbers repetitively. However, a parallel algorithm for the repetitive prime number labeling scheme does not exist; therefore, this scheme is difficult to apply to massive XML data. In this paper, a dynamic and parallel approach of XML labeling algorithm that works with MapReduce is proposed for, particularly, the repetitive prime number labeling scheme. Two optimization techniques are devised: the label assignment order adjustment to further reduce the label size and the upper tree compressing technique to reduce the memory requirements during the labeling process. Experiments over real-world XML data confirmed that the techniques are effective than the previous works.

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Acknowledgments

This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP) (No. R0101-16-0054, WiseKB: Big data based self-evolving knowledge base and reasoning platform) and Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT & Future Planning (NRF-2014R1A1A1002236) and ETRI R&D Program (“Development of Big Data Platform for Dual Mode Batch-Query Analytics, 16ZS1410”) funded by the Government of Korea.

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Authors and Affiliations

  1. Biomedical Knowledge Engineering Laboratory and Dental Research Institute, Seoul National University, Seoul, Korea

    Jinhyun Ahn

  2. Department of Computer and Information Engineering, Hoseo University, Asan, Korea

    Dong-Hyuk Im

  3. BigData Intelligence Research Department, Electronics and Telecommunications Research Institute, Daejon, Korea

    Taewhi Lee

  4. Biomedical Knowledge Engineering Laboratory, Dental Research Institute and Institute of Human-Environment Interface Biology, Seoul National University, Seoul, Korea

    Hong-Gee Kim

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  1. Jinhyun Ahn
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  2. Dong-Hyuk Im
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  3. Taewhi Lee
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  4. Hong-Gee Kim
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Correspondence to Dong-Hyuk Im.

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Ahn, J., Im, DH., Lee, T. et al. A dynamic and parallel approach for repetitive prime labeling of XML with MapReduce. J Supercomput 73, 810–836 (2017). https://doi.org/10.1007/s11227-016-1803-y

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  • DOI: https://doi.org/10.1007/s11227-016-1803-y

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