Detecting coherent explorations in SQL workloads
作者：
Highlights：
• We propose an approach for segmenting a SQL workload into meaningful, coherent database explorations.
• We describe each query of a SQL workload with a set of features that are intrinsic to a query or that relate the query to its neighbor in the workload.
• We define a set of similarity indexes, exploiting query features, that quantify the similarity between contiguous queries from several points of view.
• We proposed three methods for workload segmentation: unsupervised learning, supervised learning and weak supervision.
摘要
•We propose an approach for segmenting a SQL workload into meaningful, coherent database explorations.•We describe each query of a SQL workload with a set of features that are intrinsic to a query or that relate the query to its neighbor in the workload.•We define a set of similarity indexes, exploiting query features, that quantify the similarity between contiguous queries from several points of view.•We proposed three methods for workload segmentation: unsupervised learning, supervised learning and weak supervision.
论文关键词：
论文评审过程：Received 9 July 2019, Revised 19 October 2019, Accepted 24 November 2019, Available online 9 December 2019, Version of Record 10 June 2020.
论文官网地址：https://doi.org/10.1016/j.is.2019.101479
