Parallel labeling of massive XML data with MapReduce

  • Authors:

  • Hyebong Choi;Kyong-Ha Lee;Yoon-Joon Lee

  • Affiliations:

  • Department of Computer Science, KAIST, Yuseong-gu, Daejeon, Republic of Korea 305-701;Intelligent Convergence Media Research Department, Broadcasting & Telecommunications Media Research Laboratory, ETRI, Yuseong-gu, Daejeon, Republic of Korea 305-700;Department of Computer Science, KAIST, Yuseong-gu, Daejeon, Republic of Korea 305-701

  • Venue:
  • The Journal of Supercomputing
  • Year:
  • 2014

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Abstract

The volume of XML data has become enormous and still grows very quickly as many data have been typed in XML by virtue of its simplicity and extensibility. While a tree labeling algorithm has a crucial role in XML query processing, conventional algorithms are all sequential so that they fail to label a large volume of XML data in a timely manner. To address this issue, we devise parallel tree labeling algorithms for massive XML data. Specifically, we focus on how to efficiently label a single large XML file in parallel. We first propose parallel versions of two prominent tree labeling schemes based on the MapReduce framework. We then present techniques for runtime workload balancing and data repartition to solve performance issues caused by data skewness and MapReduce's inherited limitation. Through extensive experiments with synthetic and real-world datasets on 15 nodes, we show that our parallel labeling algorithms are up to 17 times faster than conventional algorithms, providing strong durability against data skewness.