Mining SQL workloads for learning analysis behavior
作者：
Highlights：
• We propose an approach for learning analysis patterns in SQL workloads.
• We define a set of similarity measures tailored for SQL queries and explorations.
• We cluster similar explorations using an innovative clustering process.
• We use a large palette of indicators for profiling and analyzing users’ behavior.
• We conduct a large experimental evaluation of the proposal over SQLShare workload.
摘要
•We propose an approach for learning analysis patterns in SQL workloads.•We define a set of similarity measures tailored for SQL queries and explorations.•We cluster similar explorations using an innovative clustering process.•We use a large palette of indicators for profiling and analyzing users’ behavior.•We conduct a large experimental evaluation of the proposal over SQLShare workload.
论文关键词：Data exploration,SQLShare,Human analysis behavior,Sequence analysis,Statistical explainability,Visual indicators
论文评审过程：Received 10 July 2021, Revised 28 January 2022, Accepted 10 February 2022, Available online 15 February 2022, Version of Record 12 May 2022.
论文官网地址：https://doi.org/10.1016/j.is.2022.102004
