Erebus: Explaining the Outputs of Data Streaming Queries
Dimitris Palyvos-Giannas, Katerina Tzompanaki, Marina Papatriantafilou, Vincenzo Gulisano
摘要
In data streaming, why-provenance can explain why a given outcome is observed but offers no help in understanding why an expected outcome is missing. Explaining missing answers has been addressed in DBMSs, but these solutions are not directly applicable to the streaming setting, because of the extra challenges posed by limited storage and by the unbounded nature of data streams.
With our framework, Erebus , we tackle the unaddressed challenges behind explaining missing answers in streaming applications. Erebus allows users to define expectations about the results of a query, verifying at runtime if such expectations hold, and also providing explanations when expected and observed outcomes diverge (missing answers). To the best of our knowledge, Erebus is the first such solution in data streaming. Our thorough evaluation on real data shows that Erebus can explain the (missing) answers with small overheads, both in low- and higher-end devices, even when large portions of the processed data are part of such explanations.
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它引用的顶会 Paper3
- Ananke: A Streaming Framework for Live Forward ProvenanceDimitris Palyvos-Giannas, Bastian Havers, Marina Papatriantafilou, Vincenzo GulisanoVLDB 2021 · 被引用 21 次
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