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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abouzeid, A., Bajda-Pawlikowski, K., Abadi, D., Silberschatz, A., Rasin, A.: HadoopDB: an architectural hybrid of MapReduce and DBMS technologies for analytical workloads. In: VLDB, pp. 922–933 (2009)
Bidoit, N., Colazzo, D., Malla, N., Ulliana, F., Nole, M., Sartiani, C.: Processing XML queries and updates on map/reduce clusters. In: EDBT, pp. 745–748 (2013)
Blanas, S., Patel, J.M., Ercegovac, V., Rao, J., Shekita, E.J., Tian, Y.: A comparison of join algorithms for log processing in MapReduce. In: SIGMOD, pp. 975–986 (2010)
Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: optimal XML pattern matching. In: SIGMOD, pp. 310–321 (2002)
Choi, H., Lee, K., Kim, S., Lee, Y., Moon, B.: HadoopXML: a suite for parallel processing of massive XML data with multiple twig pattern queries. In: CIKM, pp. 2737–2739 (2012)
Cong, G., Fan, W., Kementsietsidis, A., Li, J., Liu, X.: Partial evaluation for distributed XPath query processing and beyond. ACM Trans. Database Syst. 37(4), 32 (2012)
Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. In: USENIX Symp. on Operating System Design and Implementation, pp. 137–150 (2004)
Kling, P., Ozsu, M.T., Daudjee, K.: Generating efficient excution plans for vertically partitioned XML databases. PVLDB 4(1), 1–11 (2010)
Lin, Y., Agrawa, D., Chen, C., Ooi, B.C., Wu, S.: Llama: leveraging columnar starage for scalable join processing in the MapReduce framework. In: SIGMOD, pp. 961–972 (2011)
Okcan, A., Riedewald, M.: Pricessing theta-joins using MapReduce. In: SIGMOD, pp. 949–960 (2011)
Suciu, D.: Distributed query evaluation on semistricutred data. ACM Trans. Database Syst. 27(1), 1–62 (2002)
Wu, H.: Parallelizing Structural Joins to Process Queries over Big XML Data Using MapReduce. Tech Report, http://www1.i2r.a-star.edu.sg/~huwu/paraSJ.pdf
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wu, H. (2014). Parallelizing Structural Joins to Process Queries over Big XML Data Using MapReduce. In: Decker, H., Lhotská, L., Link, S., Spies, M., Wagner, R.R. (eds) Database and Expert Systems Applications. DEXA 2014. Lecture Notes in Computer Science, vol 8645. Springer, Cham. https://doi.org/10.1007/978-3-319-10085-2_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-10085-2_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10084-5
Online ISBN: 978-3-319-10085-2
eBook Packages: Computer ScienceComputer Science (R0)