Evaluating Continuous Queries with Inconsistency Annotations
Summary: Proposes provenance-based inconsistency annotations for continuous queries, mapping input-stream constraint violations to CQ results to preserve continuous processing instead of dropping or repairing tuples. Graph-based approach yields stable throughput under heavy window/memory loads (10–80% gains vs baselines) with ≤25% runtime overhead. (summarized by gpt-5-mini on Feb 09 2026)
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