Abstract
Processing XML queries over big XML data using MapReduce has been studied in recent years. However, the existing works focus on partitioning XML documents and distributing XML fragments into different compute nodes. This attempt may introduce high overhead in XML fragment transferring from one node to another during MapReduce execution. Motivated by the structural join based XML query processing approach, which uses only related inverted lists to process queries in order to reduce I/O cost, we propose a novel technique to use MapReduce to distribute labels in inverted lists in a computing cluster, so that structural joins can be parallelly performed to process queries. We also propose an optimization technique to reduce the computing space in our framework, to improve the performance of query processing. Last, we conduct experiment to validate our algorithms.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
