Data Provenance Aggregation and Structural Descriptors for Query-Template Identification
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 S...
| Authors | Paulo Pintor, Rogério Costa, José Moreira |
|---|---|
| Primary category | cs.DB |
| Published | 15 October 2026 |
| Last updated at source | 15 October 2026 |
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