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AI Meets AI: Leveraging Query Executions to Improve Index Recommendations.

, , , , , and . SIGMOD Conference, page 1241-1258. ACM, (2019)

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Towards Full Stack Adaptivity in Permissioned Blockchains., , , , , and . Proc. VLDB Endow., 17 (5): 1073-1080 (January 2024)ARDA: Automatic Relational Data Augmentation for Machine Learning., , , , , and . Proc. VLDB Endow., 13 (9): 1373-1387 (2020)NashDB: Fragmentation, Replication, and Provisioning using Economic Methods., , , , and . Proc. VLDB Endow., 12 (12): 1830-1833 (2019)CDFShop: Exploring and Optimizing Learned Index Structures., , and . SIGMOD Conference, page 2789-2792. ACM, (2020)aiDM'22: Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management., , , , and . SIGMOD Conference, page 2550-2551. ACM, (2022)A Learning-Based Service for Cost and Performance Management of Cloud Databases., , and . ICDE, page 1361-1362. IEEE Computer Society, (2017)Workload management for cloud databases via machine learning., and . ICDE Workshops, page 27-30. IEEE Computer Society, (2016)Stage: Query Execution Time Prediction in Amazon Redshift., , , , , , , , , and . SIGMOD Conference Companion, page 280-294. ACM, (2024)QO-Insight: Inspecting Steered Query Optimizers., , , , , , , , and . Proc. VLDB Endow., 16 (12): 3922-3925 (2023)Robust Query Driven Cardinality Estimation under Changing Workloads., , , , , , , and . Proc. VLDB Endow., 16 (6): 1520-1533 (2023)