| Cardinality Estimation in DBMS: A Comprehensive Benchmark Evaluation Y Han*, Z Wu*, P Wu, R Zhu, J Yang, LW Tan, K Zeng, G Cong, Y Qin, ... Proceedings of the VLDB Endowment 15 (2150-8097), 752-765, 2021 | 124 | 2021 |
| FLAT: Fast, Lightweight and Accurate Method for Cardinality Estimation R Zhu*, Z Wu*, Y Han, K Zeng, A Pfadler, Z Qian, J Zhou, B Cui Proceedings of the VLDB Endowment 14 (2150-8097), 1489 - 1502, 2020 | 105 | 2020 |
| Robust query driven cardinality estimation under changing workloads P Negi, Z Wu, A Kipf, N Tatbul, R Marcus, S Madden, T Kraska, ... Proceedings of the VLDB Endowment 16 (6), 1520-1533, 2023 | 53 | 2023 |
| Bayescard: Revitilizing bayesian frameworks for cardinality estimation Z Wu, A Shaikhha, R Zhu, K Zeng, Y Han, J Zhou arXiv preprint arXiv:2012.14743, 2020 | 53 | 2020 |
| Lero: A learning-to-rank query optimizer R Zhu, W Chen, B Ding, X Chen, A Pfadler, Z Wu, J Zhou Proceedings of the VLDB Endowment 16 (6), 1466-1479, 2023 | 50 | 2023 |
| FactorJoin: A New Cardinality Estimation Framework for Join Queries Z Wu, P Negi, M Alizadeh, T Kraska, S Madden SIGMOD 2023, 2022 | 43 | 2022 |
| A Unified Transferable Model for ML-Enhanced DBMS Z Wu, P Yang, P Yu, R Zhu, Y Han, Y Li, D Lian, K Zeng, J Zhou Conference on Innovative Data Systems Research (CIDR), 2021 | 38 | 2021 |
| Efficient and scalable structure learning for Bayesian networks: Algorithms and applications R Zhu, A Pfadler, Z Wu, Y Han, X Yang, F Ye, Z Qian, J Zhou, B Cui 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2613-2624, 2021 | 19 | 2021 |
| Learned Query Optimizer: At the Forefront of AI-Driven Databases. R Zhu, Z Wu, C Chai, A Pfadler, B Ding, G Li, J Zhou EDBT, 1-4, 2022 | 17 | 2022 |
| PATROL: A Velocity Control Framework for Autonomous Vehicle via Spatial-Temporal Reinforcement Learning Z Xu, S Liu, Z Wu, X Chen, K Zeng, K Zheng, H Su ACM International Conference on Information and Knowledge Management, 2021 | 16 | 2021 |
| FSPN: a new class of probabilistic graphical model Z Wu, R Zhu, A Pfadler, Y Han, J Li, Z Qian, K Zeng, J Zhou arXiv preprint arXiv:2011.09020, 2020 | 16* | 2020 |
| Check out the big brain on BRAD: simplifying cloud data processing with learned automated data meshes T Kraska, T Li, S Madden, M Markakis, A Ngom, Z Wu, GX Yu Proceedings of the VLDB Endowment 16 (11), 3293-3301, 2023 | 13 | 2023 |
| Extract-transform-load for video streams F Kossmann, Z Wu, E Lai, N Tatbul, L Cao, T Kraska, S Madden arXiv preprint arXiv:2310.04830, 2023 | 12 | 2023 |
| HeGA: heterogeneous graph aggregation network for trajectory prediction in high-density traffic S Liu, X Chen, Z Wu, L Deng, H Su, K Zheng Proceedings of the 31st ACM International Conference on Information …, 2022 | 12 | 2022 |
| Stage: Query Execution Time Prediction in Amazon Redshift Z Wu, R Marcus, Z Liu, P Negi, V Nathan, P Pfeil, G Saxena, M Rahman, ... Companion of the 2024 International Conference on Management of Data, 280-294, 2024 | 11 | 2024 |
| Blueprinting the Cloud: Unifying and Automatically Optimizing Cloud Data Infrastructures with BRAD XY Geoffrey, Z Wu, F Kossmann, T Li, M Markakis, A Ngom, S Madden, ... Proceedings of the VLDB Endowment 17 (11), 3629-3643, 2024 | 4* | 2024 |
| OS Pre-trained Transformer: Predicting Query Latencies across Changing System Contexts P Negi, Z Wu, A Nasr-Esfahany, H Sharma, M Alizadeh, T Kraska, ... Submission, 2024 | 2 | 2024 |
| Complexity and diversity in sparse code priors improve receptive field characterization of Macaque V1 neurons Z Wu, H Rockwell, Y Zhang, S Tang, TS Lee PLoS computational biology 17 (10), e1009528, 2021 | 2 | 2021 |
| Improving DBMS Scheduling Decisions with Fine-grained Performance Prediction on Concurrent Queries--Extended Z Wu, M Markakis, C Liu, PB Chen, B Narayanaswamy, T Kraska, ... arXiv preprint arXiv:2501.16256, 2025 | | 2025 |
| CascadeServe: Unlocking Model Cascades for Inference Serving F Kossmann, Z Wu, A Turk, N Tatbul, L Cao, S Madden arXiv preprint arXiv:2406.14424, 2024 | | 2024 |
Tim KraskaMITEmail yang diverifikasi di mit.edu