Imam Machdi
ABSTRACT
Parallel XML query processing systems that process numerous queries over large heterogeneous XML documents often experience under-performance due to workload imbalance and low CPU/system utilization, because conventional partitioning strategies cannot serve well for state-of-the-art query processing algorithms, such as holistic twig joins. Consequently, partitioning and distributing heterogeneous XML documents onto a parallel cluster system have lead to such an intricacy issue for maintaining good query performance. In this paper, we propose XML data partitioning strategies that are able to alleviate system performance degradation due to workload imbalance, especially for parallel holistic twig joins processing. The proposed XML data partitioning strategies aim at improving workload balance on both static data distribution and dynamic data distribution. In the first strategy we refine an XML partition having a high cost by series of XML data partition refinements with various levels of granularities from document, query, and subquery, up to node streams. The selection of the granularity level for refining a high cost partition is contextually dependent on the overall workload balance in the system. In the second strategy for dynamic data distribution, we dynamically handle low system utilization when there are many idle nodes in the system. We propose an XML data redistribution approach by partitioning XML data on the fly at the stream nodes-based granularity.
- Niagara query engine. http://www.cs.wisc.edu/niagara.Google Scholar
- Stanford university infolab. http://infolab.stanford.edu/pub/movies/dtd.html.Google Scholar
- S. Al-Khalifa, H. V. Jagadish, J. M. Patel, Y. Wu, N. Koudas, and D. Srivastava. Structural Joins: A Primitive for Efficient XML Query Pattern Matching. In Proc. of the 18th International Conference on Data Engineering (ICDE'02), pages 141--, 2002. Google ScholarDigital Library
- T. Amagasa, K. Kido, and H. Kitagawa. Querying XML Data Using PC Cluster System. In Proc. of the International Workshops on XML Data Management Tools and Techniques (XANTEC'07), pages 5--9, 2007. Google ScholarDigital Library
- J.-M. Bremer and M. Gertz. On Distributing XML Repositories. In International Workshop on the Web and Databases (WebDB), pages 73--78, 2003.Google Scholar
- N. Bruno, N. Koudas, and D. Srivastava. Holistic Twig Joins: Optimal XML Pattern Matching. In Proc. of the 2002 ACM SIGMOD International Conference on Management of Data (SIGMOD'02), pages 310--321, 2002. Google ScholarDigital Library
- T. Chen, J. Lu, and T. W. Ling. On Boosting Holism in XML Twig Pattern Matching Using Structural Indexing Techniques. In Proc. of the 24th ACM SIGMOD International Conference on Management of Data (SIGMOD'05), pages 455--466, 2005. Google ScholarDigital Library
- R. Goldman and J. Widom. DataGuides: Enabling Query Formulation and Optimization in Semistructured Databases. In Proc. of 23rd International Conference on Very Large Data Bases (VLDB'97), pages 436--445, 1997. Google ScholarDigital Library
- G. Gou and R. Chirkova. Efficiently Querying Large XML Data Repositories: A Survey. IEEE Transactions on Knowledge and Data Engineering, 19(10):1381--1403, 2007. Google ScholarDigital Library
- R. Kaushik, R. Krishnamurthy, J. F. Naughton, and R. Ramakrishnan. On the Integration of Structure Indexes and Inverted Lists. In Proc. of the 23rd ACM SIGMOD International Conference on Management of Data (SIGMOD'04), pages 779--790, 2004. Google ScholarDigital Library
- K. Kido, T. Amagasa, and H. Kitagawa. Processing XPath Queries in PC Clusters Using XML Data Partitioning. In Proc. of the 22nd ICDE Workshops, page 114, 2006. Google ScholarDigital Library
- H. Kurita, K. Hatano, J. Miyazaki, and S. Uemura. Efficient Query Processing for Large XML Data in Distributed Environments. In Proc. of the 21st International Conference on Advanced Networking and Applications (AINA'07), 2007. Google ScholarDigital Library
- Y. G. Li, S. Bressan, G. Dobbie, Z. Lacroix, M. L. Lee, U. Nambiar, and B. Wadhwa. XOO7: Applying OO7 Benchmark to XML Query Processing Tool. In Proc. of the 10th International Conference on Information and Knowledge Management, pages 167--174, New York, NY, USA, 2001. Google ScholarDigital Library
- J. Lu, T. Chen, and T. W. Ling. Efficient Processing of XML Twig Patterns with Parent Child Edges: A Look-ahead Approach. In Proc. of 13th International Conference on Information and Knowledge Management (CIKM'04), pages 533--542, 2004. Google ScholarDigital Library
- K. Lu, Y. Zhu, and W. Sun. Parallel Processing XML Documents. In Proc. of International Database Engineering and Applications Symposium (IDEAS), pages 96--105, 2002. Google ScholarDigital Library
- I. Machdi, T. Amagasa, and H. Kitagawa. GMX: An XML Data Partitioning Scheme for Holistic Twig Joins. In Proc. of 10th International Conference on Information Integration and Web-based Applications and Services (iiWAS'08), Linz, Austria, 2008. Google ScholarDigital Library
- R. Sakellariou and J. R. Gurd. Compile-time Minimisation of Load Imbalance in Loop Nests. In Proc. of the 11th International Conference on Supercomputing, pages 277--284, 1997. Google ScholarDigital Library
- A. Schmidt, F. Waas, M. Kersten, M. J. Carey, I. Manolescu, and R. Busse. XMark: A Benchmark for XML Data Management. In Proceedings of the 28th International Conference on Very Large Data Bases, pages 974--985. VLDB Endowment, 2002. Google ScholarDigital Library
- N. Tang, G. Wang, X. J. Yu, K.-F. Wong, and G. Yu. WIN: An Efficient Data Placement Strategy for Parallel XML Databases. In Proc. of 11th International Conference on Parallel and Distributed Systems (ICPADS), pages 249--355, 2005. Google ScholarDigital Library
- H. Wang, S. Park, W. Fan, and P. S. Yu. Vist: A Dynamic Index Method for Querying XML Data by Tree Structures. In Proc. of the 22nd ACM SIGMOD International Conference on Management of Data (SIGMOD'03), pages 110--121, 2003. Google ScholarDigital Library
- C. Zhang, J. F. Naughton, D. J. DeWitt, Q. Luo, and G. M. Lohman. On Supporting Containment Queries in Relational Database Management Systems. In Proc. of the 2001 ACM SIGMOD International Conference on Management of data, pages 425--436, 2001. Google ScholarDigital Library
Index Terms
- XML data partitioning strategies to improve parallelism in parallel holistic twig joins
Recommendations
GMX: an XML data partitioning scheme for holistic twig joins
iiWAS '08: Proceedings of the 10th International Conference on Information Integration and Web-based Applications & ServicesAs traditional partitioning strategies do not serve well for semistructured data, partitioning and distributing heterogeneous XML documents onto a parallel cluster system have lead to such an intricacy issue for maintaining good query processing ...
Holistic twig joins: optimal XML pattern matching
SIGMOD '02: Proceedings of the 2002 ACM SIGMOD international conference on Management of dataXML employs a tree-structured data model, and, naturally, XML queries specify patterns of selection predicates on multiple elements related by a tree structure. Finding all occurrences of such a twig pattern in an XML database is a core operation for ...
Cost-based holistic twig joins
An evaluation of XML queries such as XQuery or XPath expressions represents a challenging task due to its complexity. Many algorithms have been introduced to cope with this problem. Some of them, called binary joins, evaluate separated parts of a query ...
Comments