Ettu中的大型查询处理综述
Summarizing Large Query Logs in Ettu
摘要：
数据库访问日志很大，很难处理，而且很难让人检查和总结。尽管如此，它们仍然是从性能调优到安全审计等任务的标准参考资源。在本文中，我们解决了复杂的编码大序列的SQL查询呈现给人类用户的挑战。我们的方法是基于Weisfeiler-Lehman（WL）的近似图同构算法，它确定一个图形的显着特征，或者在我们的情况下，一个抽象的语法树。我们的推广WL允许我们定义一个SQL查询的距离度量，这反过来又允许自动聚类的查询。我们还提出了两种可视化查询集群的技术，和一个算法，允许这些可视化以交互式的速度构建。最后，我们评估我们的算法的背景下，一个激励的例子：在美国一家大型银行的内部威胁检测。我们在真实世界的查询日志上进行了实验，结果表明：（a）我们的距离度量捕获了一个有意义的相似性概念，（b）日志摘要过程是可扩展的和高性能的。
英文摘要：
Database access logs are large, unwieldy, and hard for humans to inspect and summarize. In spite of this, they remain the canonical go-to resource for tasks ranging from performance tuning to security auditing. In this paper, we address the challenge of compactly encoding large sequences of SQL queries for presentation to a human user. Our approach is based on the Weisfeiler-Lehman (WL) approximate graph isomorphism algorithm, which identifies salient features of a graph or in our case of an abstract syntax tree. Our generalization of WL allows us to define a distance metric for SQL queries, which in turn permits automated clustering of queries. We also present two techniques for visualizing query clusters, and an algorithm that allows these visualizations to be constructed at interactive speeds. Finally, we evaluate our algorithms in the context of a motivating example: insider threat detection at a large US bank. We show experimentally on real world query logs that (a) our distance metric captures a meaningful notion of similarity, and (b) the log summarization process is scalable and performant.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
