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Peering through the Dark: An Owl's View of Inter-job Dependencies and Jobs' Impact in Shared Clusters., , , , , and . SIGMOD Conference, page 1889-1892. ACM, (2019)Reservation-based Scheduling: If You're Late Don't Blame Us!, , , , , and . SoCC, page 2:1-2:14. ACM, (2014)Schema Matching using Pre-Trained Language Models., , , , , , , and . ICDE, page 1558-1571. IEEE, (2023)Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML., , , , , , , , , and 12 other author(s). CoRR, (2019)Doppler: Automated SKU Recommendation in Migrating SQL Workloads to the Cloud., , , , , , , , , and 1 other author(s). Proc. VLDB Endow., 15 (12): 3509-3521 (2022)Vamsa: Tracking Provenance in Data Science Scripts., , , , , and . CoRR, (2020)Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML.NET Pipelines., , , , , , , , , and 4 other author(s). SIGMOD Rec., 51 (2): 30-37 (2022)Data Science through the looking glass and what we found there., , , , , , , , , and 1 other author(s). CoRR, (2019)Vamsa: Automated Provenance Tracking in Data Science Scripts., , , , , , , and . KDD, page 1542-1551. ACM, (2020)PerfOrator: eloquent performance models for Resource Optimization., , , and . SoCC, page 415-427. ACM, (2016)