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Erebus: Explaining the Outputs of Data Streaming Queries

Summary: Erebus brings why-not provenance to unbounded streams: users specify runtime expectations and receive explanations for missing answers without replaying or storing all data. It distinguishes absent from not-yet-produced results under tight resource budgets, with low measured overhead. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13268
Venue
VLDB
Year
2023
Pagerank
5.484341e-05
Overall Rank
8,110 | 44.36%
DOI
10.14778/3565816.3565825

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{palyvosgiannas_vldb23,
        title = {{Erebus: Explaining the Outputs of Data Streaming Queries}},
        author = {Palyvos-Giannas, Dimitris and Tzompanaki, Katerina and Papatriantafilou, Marina and Gulisano, Vincenzo},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {2},
        pages = {230--242},
        doi = {10.14778/3565816.3565825},
        url = {https://doi.org/10.14778/3565816.3565825},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,320 LPStream: Fine-grained Lazy Provenance for Stream Processing 2026 SIGMOD 5.093636e-05
10,818 Evaluating Continuous Queries with Inconsistency Annotations 2025 VLDB 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 7 of 7 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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