| Selecting subexpressions to materialize at datacenter scale A Jindal, K Karanasos, S Rao, H Patel Proceedings of the VLDB Endowment 11 (7), 800-812, 2018 | 125 | 2018 |
| Cost models for big data query processing: Learning, retrofitting, and our findings T Siddiqui, A Jindal, S Qiao, H Patel, W Le Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 98 | 2020 |
| Computation reuse in analytics job service at microsoft A Jindal, S Qiao, H Patel, Z Yin, J Di, M Bag, M Friedman, Y Lin, ... Proceedings of the 2018 International Conference on Management of Data, 191-203, 2018 | 76 | 2018 |
| Towards a learning optimizer for shared clouds C Wu, A Jindal, S Amizadeh, H Patel, W Le, S Qiao, S Rao Proceedings of the VLDB Endowment 12 (3), 210-222, 2018 | 66 | 2018 |
| Wangchao Le, Shi Qiao, and Sriram Rao. 2018. Towards a learning optimizer for shared clouds C Wu, A Jindal, S Amizadeh, H Patel Proceedings of the VLDB Endowment 12 (3), 210-222, 2018 | 61 | 2018 |
| Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML A Agrawal, R Chatterjee, C Curino, A Floratou, N Gowdal, M Interlandi, ... arXiv preprint arXiv:1909.00084, 2019 | 39 | 2019 |
| Analyzing multiple data streams as a single data object EJ Triou, F Xu, H Patel, J Zhou US Patent 10,565,208, 2020 | 38 | 2020 |
| Magpie: Python at Speed and Scale using Cloud Backends. A Jindal, KV Emani, M Daum, O Poppe, B Haynes, A Pavlenko, A Gupta, ... CIDR, 2021 | 36 | 2021 |
| Peregrine: Workload optimization for cloud query engines A Jindal, H Patel, A Roy, S Qiao, Z Yin, R Sen, S Krishnan Proceedings of the ACM Symposium on Cloud Computing, 416-427, 2019 | 34 | 2019 |
| Deploying a steered query optimizer in production at microsoft W Zhang, M Interlandi, P Mineiro, S Qiao, N Ghazanfari, K Lie, ... Proceedings of the 2022 International Conference on Management of Data, 2299 …, 2022 | 26 | 2022 |
| Microlearner: A fine-grained learning optimizer for big data workloads at microsoft A Jindal, S Qiao, R Sen, H Patel 2021 IEEE 37th International Conference on Data Engineering (ICDE), 2423-2434, 2021 | 26 | 2021 |
| Autotoken: Predicting peak parallelism for big data analytics at microsoft R Sen, A Jindal, H Patel, S Qiao Proceedings of the VLDB Endowment 13 (12), 3326-3339, 2020 | 25 | 2020 |
| Optimal resource allocation for serverless queries A Pimpley, S Li, A Srivastava, V Rohra, Y Zhu, S Srinivasan, A Jindal, ... arXiv preprint arXiv:2107.08594, 2021 | 18 | 2021 |
| Learned resource consumption model for optimizing big data queries TA Siddiqui, A Jindal, Q Shi, HS Patel US Patent App. 16/511,966, 2020 | 18 | 2020 |
| Hyper dimension shuffle: Efficient data repartition at petabyte scale in scope S Qiao, A Nicoara, J Sun, M Friedman, H Patel, J Ekanayake Proceedings of the VLDB Endowment 12 (10), 1113-1125, 2019 | 16 | 2019 |
| Towards plan-aware resource allocation in serverless query processing M Bag, A Jindal, H Patel 12th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 20), 2020 | 14 | 2020 |
| Sparkcruise: Handsfree computation reuse in spark A Roy, A Jindal, H Patel, A Gosalia, S Krishnan, C Curino Proceedings of the VLDB Endowment 12 (12), 1850-1853, 2019 | 13 | 2019 |
| Selection of subexpressions to materialize for datacenter scale A Jindal, K Karanasos, HS Patel, SRAO Sriram US Patent 10,726,014, 2020 | 9 | 2020 |
| Phoebe: a learning-based checkpoint optimizer Y Zhu, M Interlandi, A Roy, K Das, H Patel, M Bag, H Sharma, A Jindal arXiv preprint arXiv:2110.02313, 2021 | 8 | 2021 |
| Resource optimization for serverless query processing HS Patel, Q Shi, A Jindal, MK Bag, R Sen, CA Curino US Patent 11,455,192, 2022 | 7 | 2022 |
