Abstract
Under the premise of accelerating statistics by RDF storage index and narrowing the scope of semantic pruning, the distance-based triple pattern reordering algorithm is used to obtain the optimal connection order of a single query. After converting each query to a left deep tree, the left deep tree identification algorithm is used to find the common subtree and evaluate its cost. Establish materialized view and corresponding update replacement mechanism for valuable common subtrees. While optimizing a single query to a certain extent, it improves the possibility of the existence of common result sets among multiple queries. By making full use of query sharing, the overall execution cost of query set can be reduced. The experimental results show that the algorithm of this paper has better query performance than the existing query schemes, whether it is on single query or multiple query. Especially in the case that the RDF data set is large in scale, the number of queries in the query set is large, and the query statement is more complicated, the multi-query optimization method of this paper is better.
F. Xu—The main research areas are semantic data management and query optimization.
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Acknowledgment
I am very grateful to my instructors Jinguang Gu, Fangfang Xu and Haidong Fu for their help and guidance.
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Wang, M., Xu, F., Fu, H. (2020). RDF Multi-query Optimization Algorithm Based on Triple Pattern Reordering. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1075. Springer, Cham. https://doi.org/10.1007/978-3-030-32591-6_26
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DOI: https://doi.org/10.1007/978-3-030-32591-6_26
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