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Efficient Querying Distributed Big-XML Data using MapReduce

Efficient Querying Distributed Big-XML Data using MapReduce

Song Kunfang, Hongwei Lu
Copyright: © 2016 |Volume: 8 |Issue: 3 |Pages: 10
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466690004|DOI: 10.4018/IJGHPC.2016070105
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MLA

Kunfang, Song, and Hongwei Lu. "Efficient Querying Distributed Big-XML Data using MapReduce." IJGHPC vol.8, no.3 2016: pp.70-79. http://doi.org/10.4018/IJGHPC.2016070105

APA

Kunfang, S. & Lu, H. (2016). Efficient Querying Distributed Big-XML Data using MapReduce. International Journal of Grid and High Performance Computing (IJGHPC), 8(3), 70-79. http://doi.org/10.4018/IJGHPC.2016070105

Chicago

Kunfang, Song, and Hongwei Lu. "Efficient Querying Distributed Big-XML Data using MapReduce," International Journal of Grid and High Performance Computing (IJGHPC) 8, no.3: 70-79. http://doi.org/10.4018/IJGHPC.2016070105

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Abstract

MapReduce is a widely adopted computing framework for data-intensive applications running on clusters. This paper proposed an approach to exploit data parallelisms in XML processing using MapReduce in Hadoop. The authors' solution seamlessly integrates data storage, labeling, indexing, and parallel queries to process a massive amount of XML data. Specifically, the authors introduce an SDN labeling algorithm and a distributed hierarchical index using DHTs. More importantly, an advanced two-phase MapReduce solution are designed that is able to efficiently address the issues of labeling, indexing, and query processing on big XML data. The experimental results show the efficiency and effectiveness of the proposed parallel XML data approach using Hadoop.

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