Abstract
Twig query processing is one of the core operations of XML queries. Centralized holistic twig algorithms suffer great efficiency losses when large-scale XML documents are partitioned and stored in the cloud. Previous work on distributed twig query processing have some limitations, e.g., utter dependence on priori knowledge of query patterns, iteration of MapReduce jobs, etc. In this paper, our arbitrary XML partitioning and storage strategy require no knowledge of query pattern; twig queries can be efficiently processed in a single-round MapReduce job with good scalability. Extensive experiments are conducted to verify the efficiency and scalability of our algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Al-Khalifa, S., Jagadish, H., Koudas, N., Patel, J., Srivastava, D., Wu, Y.: Structural joins: A primitive for efficient XML query pattern matching. In: Proceedings of the 18th International Conference on Data Engineering, pp. 141–152. IEEE Computer Society, Washington, DC (2002)
Bruno, N., Koudas, N., Srivastava, D.: Holistic twig joins: Optimal XML pattern matching. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, pp. 310–321. ACM, New York (2002)
Chen, S., Li, H.G., Tatemura, J., Hsiung, W.P., Agrawal, D., Candan, K.S.: Twig2stack: Bottom-up processing of generalized-tree-pattern queries over XML documents. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 283–294. VLDB Endowment (2006)
Chen, T., Lu, J., Ling, T.W.: On boosting holism in XML twig pattern matching using structural indexing techniques. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 455–466. ACM, New York (2005)
Choi, H., Lee, K.H., Kim, S.H., Lee, Y.J., Moon, B.: HadoopXML: A suite for parallel processing of massive XML data with multiple twig pattern queries. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management, CIKM 2012, pp. 2737–2739. ACM, New York (2012)
Cui, B., Mei, H., Ooi, B.C.: Big data: the driver for innovation in databases. National Science Review 1(1), 27–30 (2014)
Damigos, M., Gergatsoulis, M., Plitsos, S.: Distributed processing of XPath queries using MapReduce. In: Catania, B., et al. (eds.) New Trends in Databases and Information Systems. AISC, vol. 241, pp. 69–77. Springer, Heidelberg (2014)
Dean, J., Ghemawat, S.: Mapreduce: Simplified data processing on large clusters. In: Proceedings of the 6th Conference on Symposium on Opearting Systems Design & Implementation, Berkeley, CA, USA, vol. 6, p. 10 (2004)
Ding, L., Wang, G., Xin, J., Wang, X., Huang, S., Zhang, R.: Commapreduce: An improvement of mapreduce with lightweight communication mechanisms. Data & Knowledge Engineering 88, 224–247 (2013)
Jiang, H., Wang, W., Lu, H., Yu, J.X.: Holistic twig joins on indexed XML documents. In: Proceedings of the 29th International Conference on Very Large Data Bases, vol. 29, pp. 273–284. VLDB Endowment (2003)
Lu, J., Ling, T.W., Chan, C.Y., Chen, T.: From region encoding to extended dewey: On efficient processing of XML twig pattern matching. In: Proceedings of the 31st International Conference on Very Large Data Bases, pp. 193–204. VLDB Endowment (2005)
Machdi, I., Amagasa, T., Kitagawa, H.: Gmx: An XML data partitioning scheme for holistic twig joins. In: Proceedings of the 10th International Conference on Information Integration and Web-based Applications & Services, iiWAS 2008, pp. 137–146. ACM, New York (2008)
Machdi, I., Amagasa, T., Kitagawa, H.: XML data partitioning strategies to improve parallelism in parallel holistic twig joins. In: Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication, ICUIMC 2009, pp. 471–480. ACM, New York (2009)
Schmidt, A., Waas, F., Kersten, M., Carey, M.J., Manolescu, I., Busse, R.: Xmark: A benchmark for XML data management. In: Proceedings of the 28th International Conference on Very Large Databases, San Francisco, pp. 974–985 (2002)
Wu, H.: 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.) DEXA 2014, Part II. LNCS, vol. 8645, pp. 183–190. Springer, Heidelberg (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Bi, X., Wang, G., Zhao, X., Zhang, Z., Chen, S. (2015). Distributed XML Twig Query Processing Using MapReduce. In: Cheng, R., Cui, B., Zhang, Z., Cai, R., Xu, J. (eds) Web Technologies and Applications. APWeb 2015. Lecture Notes in Computer Science(), vol 9313. Springer, Cham. https://doi.org/10.1007/978-3-319-25255-1_17
Download citation
DOI: https://doi.org/10.1007/978-3-319-25255-1_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25254-4
Online ISBN: 978-3-319-25255-1
eBook Packages: Computer ScienceComputer Science (R0)