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Accelerating Human-in-the-loop Machine Learning: Challenges and Opportunities., , , , , and . DEEM@SIGMOD, page 9:1-9:4. ACM, (2018)Towards Globally Optimal Crowdsourcing Quality Management: The Uniform Worker Setting., , and . SIGMOD Conference, page 47-62. ACM, (2016)DataDiff: User-Interpretable Data Transformation Summaries for Collaborative Data Analysis., , , , , and . SIGMOD Conference, page 1769-1772. ACM, (2018)Anti-Freeze for Large and Complex Spreadsheets: Asynchronous Formula Computation., , , , and . SIGMOD Conference, page 1277-1294. ACM, (2019)Optimally Leveraging Density and Locality for Exploratory Browsing and Sampling., , , , , and . HILDA@SIGMOD, page 7:1-7:7. ACM, (2018)Avoiding drill-down fallacies with VisPilot: assisted exploration of data subsets., , , , and . IUI, page 186-196. ACM, (2019)Synthesizing view definitions from data., , , and . ICDT, page 89-103. ACM, (2010)Optimal Worker Quality and Answer Estimates in Crowd-Powered Filtering and Rating., , and . HCOMP, page 12-13. AAAI, (2014)Recsplorer: recommendation algorithms based on precedence mining., , , and . SIGMOD Conference, page 87-98. ACM, (2010)DataHub: Collaborative Data Science & Dataset Version Management at Scale., , , , , , and . CIDR, www.cidrdb.org, (2015)