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A MapReduce-Based Approach for Prefix-Based Labeling of Large XML Data

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Semantic Technology (JIST 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10055))

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Abstract

A massive amount of XML (Extensible Markup Language) data is available on the web, which can be viewed as tree data. One of the fundamental building blocks of information retrieval from tree data is answering structural queries. Various labeling schemes have been suggested for rapid structural query processing. We focus on the prefix-based labeling scheme that labels each node with a concatenation of its parent’s label and its child order. This scheme has been adapted in RDF (Resource Description Framework) data management systems that index RDF data in tree by grouping subjects. Recently, a MapReduce-based algorithm for the prefix-based labeling scheme was suggested. We observe that this algorithm fails to keep label size minimized, which makes the prefix-based labeling scheme difficult for massive real-world XML datasets. To address this issue, we propose a MapReduce-based algorithm for prefix-based labeling of XML data that reduces label size by adjusting the order of label assignments based on the structural information of the XML data. Experiments with real-world XML datasets show that the proposed approach is more effective than previous works.

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Notes

  1. 1.

    http://linkeddata.org.

  2. 2.

    http://www.cs.washington.edu/research/xmldatasets/www/repository.html.

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Acknowledgement

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).

Author information

Authors and Affiliations

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

    Jinhyun Ahn & Hong-Gee Kim

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

    Dong-Hyuk Im

  3. Institute of Human-Environment Interface Biology, Seoul National University, Seoul, South Korea

    Hong-Gee Kim

Authors
  1. Jinhyun Ahn
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  2. Dong-Hyuk Im
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  3. Hong-Gee Kim
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Corresponding author

Correspondence to Hong-Gee Kim .

Editor information

Editors and Affiliations

  1. Information Technology, Monash University, Melbourne, Victoria, Australia

    Yuan-Fang Li

  2. Computer Science and Technology, Nanjing University, Nanjing, China

    Wei Hu

  3. Computer Science, National University of Singapore, Singapore, Singapore

    Jin Song Dong

  4. University of Huddersfield, Huddersfield, United Kingdom

    Grigoris Antoniou

  5. Information and Communication Technology, Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  6. ISTD, Singapore University of Technology and Design, Singapore, Singapore

    Jun Sun

  7. Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Yang Liu

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Ahn, J., Im, DH., Kim, HG. (2016). A MapReduce-Based Approach for Prefix-Based Labeling of Large XML Data. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_7

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  • DOI: https://doi.org/10.1007/978-3-319-50112-3_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50111-6

  • Online ISBN: 978-3-319-50112-3

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