Toward Temporal Attribution Analytics in Dataflows
Summary: Introduces temporal attribution, a lightweight alternative to tuple-level provenance for scalable, time-focused dependency analysis in streaming dataflows. Models quantified operator exchanges via Temporal Interaction Networks, distinguishing discrete/liquid data and enabling five query types with state-based indexing. (summarized by gpt-5.6-luna on Aug 28 2026)
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Authors
- 1. Chrysanthi Kosyfaki (Hong Kong University of Science and Technology)
- 2. Ruiyuan Zhang (Hong Kong Generative Artificial Intelligence Research and Development Center)
- 3. Nikos Mamoulis (University of Ioannina)
- 4. Xiaofang Zhou (Hong Kong University of Science and Technology)
BibTeX Citation
@article{kosyfaki_vldb26,
title = {{Toward Temporal Attribution Analytics in Dataflows}},
author = {Kosyfaki, Chrysanthi and Zhang, Ruiyuan and Mamoulis, Nikos and Zhou, Xiaofang},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {10},
pages = {2831--2838},
doi = {10.14778/3828612.3828635},
url = {https://doi.org/10.14778/3828612.3828635},
year = {2026}
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