Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory


Li Wang, Guodong Wang, Journal of Information Processing Systems Vol. 17, No. 1, pp. 37-50, Feb. 2021  

https://doi.org/10.3745/JIPS.04.0205
Keywords: caching, Frequent Sub-query Trajectory, Nested Data, Query Optimization, Service Tree
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Abstract

Query applications based on nested data, the most commonly used form of data representation on the web, especially precise query, is becoming more extensively used. MapReduce, a distributed architecture with parallel computing power, provides a good solution for big data processing. However, in practical application, query requests are usually concurrent, which causes bottlenecks in server processing. To solve this problem, this paper first combines a column storage structure and an inverted index to build index for nested data on MapReduce. On this basis, this paper puts forward an optimization strategy which combines query execution service tree and frequent sub-query trajectory to reduce the response time of frequent queries and further improve the efficiency of multi-user concurrent queries on large scale nested data. Experiments show that this method greatly improves the efficiency of nested data query.


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Cite this article
[APA Style]
Wang, L. & Wang, G. (2021). Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory. Journal of Information Processing Systems, 17(1), 37-50. DOI: 10.3745/JIPS.04.0205.

[IEEE Style]
L. Wang and G. Wang, "Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory," Journal of Information Processing Systems, vol. 17, no. 1, pp. 37-50, 2021. DOI: 10.3745/JIPS.04.0205.

[ACM Style]
Li Wang and Guodong Wang. 2021. Query Optimization on Large Scale Nested Data with Service Tree and Frequent Trajectory. Journal of Information Processing Systems, 17, 1, (2021), 37-50. DOI: 10.3745/JIPS.04.0205.