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- Title
Parallel labeling of massive XML data with MapReduce.
- Authors
Choi, Hyebong; Lee, Kyong-Ha; Lee, Yoon-Joon
- 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.
- Subjects
LABELS; XML (Extensible Markup Language); MANAGEMENT science; ALGORITHMS; PARALLEL computers; CONVERGENT evolution
- Publication
Journal of Supercomputing, 2014, Vol 67, Issue 2, p408
- ISSN
0920-8542
- Publication type
Article
- DOI
10.1007/s11227-013-1008-6