Evaluating Continuous Queries with Inconsistency Annotations
Summary: Introduces provenance-based inconsistency annotations for continuous queries, tracing streaming constraint violations to affected outputs without dropping or repairing data. A graph-based implementation preserves throughput under memory pressure, with annotation overhead up to 25%. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Samuele Langhi (Lyon 1 University)
- 2. Angela Bonifati (Lyon 1 University)
- 3. Riccardo Tommasini (National Institute of Applied Sciences of Lyon)
BibTeX Citation
@article{langhi_vldb25,
title = {{Evaluating Continuous Queries with Inconsistency Annotations}},
author = {Langhi, Samuele and Bonifati, Angela and Tommasini, Riccardo},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {5},
pages = {1321--1334},
doi = {10.14778/3718057.3718062},
url = {https://doi.org/10.14778/3718057.3718062},
year = {2025}
}
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