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Why Big Data Industrial Systems Need Rules and What We Can Do About It., , , , , , , , , and 2 other author(s). SIGMOD Conference, page 265-276. ACM, (2015)Falcon: Scaling Up Hands-Off Crowdsourced Entity Matching to Build Cloud Services., , , , , , , , and . SIGMOD Conference, page 1431-1446. ACM, (2017)Smurf: Self-Service String Matching Using Random Forests., , , and . Proc. VLDB Endow., 12 (3): 278-291 (2018)From Data to Models and Back., , , , , and . DEEM@SIGMOD, page 1:1-1:4. ACM, (2020)TensorFlow Data Validation: Data Analysis and Validation in Continuous ML Pipelines., , , , , and . SIGMOD Conference, page 2793-2796. ACM, (2020)Magellan: toward building ecosystems of entity matching solutions., , , , , , , and . Commun. ACM, 63 (8): 83-91 (2020)Validating Data and Models in Continuous ML Pipelines., , , , , and . IEEE Data Eng. Bull., 44 (1): 42-50 (2021)CloudMatcher: A Hands-Off Cloud/Crowd Service for Entity Matching., , , , , , , , , and . Proc. VLDB Endow., 11 (12): 2042-2045 (2018)AutoSlicer: Scalable Automated Data Slicing for ML Model Analysis., , and . CoRR, (2022)Entity Matching Meets Data Science: A Progress Report from the Magellan Project., , , , , , , , , and 10 other author(s). SIGMOD Conference, page 389-403. ACM, (2019)