Parallel labeling of massive XML data with MapReduce

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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.
Publisher
SPRINGER
Issue Date
2014-02
Language
English
Article Type
Article
Keywords

ALGORITHM

Citation

JOURNAL OF SUPERCOMPUTING, v.67, no.2, pp.408 - 437

ISSN
0920-8542
DOI
10.1007/s11227-013-1008-6
URI
http://hdl.handle.net/10203/190076
Appears in Collection
CS-Journal Papers(저널논문)
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