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Database access logs are large, unwieldy, and hard for humans to inspect andsummarize. In spite of this, they remain the canonical go-to resource for tasksranging from performance tuning to security auditing. In this paper, we addressthe challenge of compactly encoding large sequences of SQL queries forpresentation to a human user. Our approach is based on the Weisfeiler-Lehman(WL) approximate graph isomorphism algorithm, which identifies salient featuresof a graph or in our case of an abstract syntax tree. Our generalization of WLallows us to define a distance metric for SQL queries, which in turn permitsautomated clustering of queries. We also present two techniques for visualizingquery clusters, and an algorithm that allows these visualizations to beconstructed at interactive speeds. Finally, we evaluate our algorithms in thecontext of a motivating example: insider threat detection at a large US bank.We show experimentally on real world query logs that (a) our distance metriccaptures a meaningful notion of similarity, and (b) the log summarizationprocess is scalable and performant.

Summarizing Large Query Logs in Ettu