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

  • Jinhyun Ahn
  • Dong-Hyuk Im
  • Hong-Gee KimEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10055)


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.


Resource Description Framework Label Scheme Hadoop Distribute File System MapReduce Framework Label Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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).


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Biomedical Knowledge Engineering Laboratory and Dental Research InstituteSeoul National UniversitySeoulSouth Korea
  2. 2.Department of Computer and Information EngineeringHoseo UniversityCheonanSouth Korea
  3. 3.Institute of Human-Environment Interface BiologySeoul National UniversitySeoulSouth Korea

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