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OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance From Database Query Event Logs., , , , , , , , and . Proc. VLDB Endow., 16 (12): 3662-3675 (2023)Dhalion: Self-Regulating Stream Processing in Heron., , , , and . Proc. VLDB Endow., 10 (12): 1825-1836 (2017)Dhalion in Action: Automatic Management of Streaming Applications., and . Proc. VLDB Endow., 11 (12): 2050-2053 (2018)OneProvenance: Efficient Extraction of Dynamic Coarse-Grained Provenance from Database Logs., , , , , , , , and . CoRR, (2022)Spur: Mitigating Slow Instances in Large-Scale Streaming Pipelines., , , and . SIGMOD Conference, page 2271-2285. ACM, (2020)Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML., , , , , , , , , and 12 other author(s). CoRR, (2019)LST-Bench: Benchmarking Log-Structured Tables in the Cloud., , , , , , , , and . CoRR, (2023)XTable in Action: Seamless Interoperability in Data Lakes., , , , , , and . CoRR, (2024)Demonstration of Geyser: Provenance Extraction and Applications over Data Science Scripts., , , , , , , , , and 1 other author(s). SIGMOD Conference Companion, page 123-126. ACM, (2023)Cloudy with high chance of DBMS: a 10-year prediction for Enterprise-Grade ML., , , , , , , , , and 12 other author(s). CIDR, www.cidrdb.org, (2020)