数据溯源聚合与结构描述符用于查询模板识别
Data Provenance Aggregation and Structural Descriptors for Query-Template Identification
Paulo Pintor · Rog\'erio Costa · Jos\'e Moreira
中文摘要
运行时查询相似性识别支持负载分析、重复行为识别、审计、调试及异常检测。现有方法主要依赖 SQL 文本、执行计划或结果值，未考虑输入数据及执行过程中的结果推导。该研究提出一种基于数据溯源的查询模板识别新方法。通过聚合整个查询结果的元组级 Lineage 和 Why-provenance，构建捕获显式数据依赖、出现频率及结构属性的查询级画像。同时推导溯源结构描述符（PSD），利用少量数值特征总结这些画像。基于从参数化 TPC-DS 查询模板生成的 12,500 个不同查询实例进行评估。实验表明，仅凭溯源足迹即可识别查询模板，且随着历史数据增加，准确率提升。此外，PSD 在有限训练历史和低测试处理成本下实现了高分类准确率。
关键要点
- 01现有查询相似性方法忽略输入数据及执行过程中的结果推导机制。
- 02利用元组级 Lineage 和 Why-provenance 聚合构建查询级画像以捕获数据依赖。
- 03提出溯源结构描述符（PSD），用少量数值特征高效总结查询画像。
- 04在 12,500 个 TPC-DS 实例上验证，仅凭溯源足迹即可识别查询模板。
- 05PSD 在有限训练历史和低测试成本下实现了高分类准确率。
解读
尚无解读。
原始英文摘要
arXiv:2610.22705v1 Announce Type: cross Abstract: Runtime query similarity identification supports several activities, including workload analysis, identifying recurring behaviour, auditing, debugging, and anomaly detection. Existing methods for evaluating query similarity mostly rely on SQL text, execution plans, or result values but do not account for input data or how results are derived during query execution. This paper proposes a novel approach using data provenance for query-template identification. We construct query-level profiles that capture explicit data dependencies, occurrence frequencies, and structural properties by aggregating tuple-level Lineage and Why-provenance across entire query results. We also derive Provenance Structural Descriptors (PSD), which summarise these profiles using a small set of numerical features. We evaluate our proposals using these profiles to identify query templates over \(12{,}500\) distinct query instances generated from parametrised TPC-DS query templates. The experimental evaluation shows that query templates can be identified solely from their provenance footprints, and that accuracy improves as more historical data becomes available. It also shows that PSD achieve high classification accuracy with limited training history and low test-processing cost.