| 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 | 73 | 2020 |
| 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 | 59 | 2018 |
| Computation Reuse in Analytics Job Service at Microsoft A Jindal, S Qiao, H Patel, Z Yin, J Di SIGMOD '18: Proceedings of the 2018 International Conference on Management …, 2018 | 49 | 2018 |
| 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 | 49 | 2018 |
| RBench: Application-specific RDF benchmarking S Qiao, ZM Özsoyoğlu Proceedings of the 2015 acm sigmod international conference on management of …, 2015 | 48 | 2015 |
| 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 | 33 | 2019 |
| Malay Bag, Marc Friedman, Yifung Lin, Konstantinos Karanasos, Sriram Rao, Computation Reuse in Analytics Job Service at Microsoft A Jindal, S Qiao, H Patel, Z Yin, J Di Proceedings of the 2018 International Conference on Management of Data, 2018 | 25* | 2018 |
| 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 | 21 | 2020 |
| 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 | 19 | 2021 |
| 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 | 15 | 2022 |
| 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 | 14 | 2021 |
| 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 | 14 | 2019 |
| Learned resource consumption model for optimizing big data queries TA Siddiqui, A Jindal, Q Shi, HS Patel US Patent App. 16/511,966, 2020 | 13 | 2020 |
| Production Experiences from Computation Reuse at Microsoft. A Jindal, S Qiao, H Patel, A Roy, J Leeka, B Haynes EDBT, 623-634, 2021 | 7 | 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 | 6 | 2022 |
| Platform agnostic query acceleration A Jindal, B Mozafari, P Yongjoo, B Westphal, Q Shi, M Larsen, AA Dixit US Patent 11,567,936, 2023 | 5 | 2023 |
| Cloud based query workload optimization HS Patel, R Sen, Z Yin, Q Shi, ROY Abhishek, A Jindal, SV Krishnan, ... US Patent App. 16/581,905, 2021 | 5 | 2021 |
| Integrated querying of disparate association and interaction data in biomedical applications S Qiao, M Koyutürk, ZM Özsoyoğlu Proceedings of the 6th ACM Conference on Bioinformatics, Computational …, 2015 | 4 | 2015 |
| One Size Does not Fit All: When to Use Signature-based Pruning to Improve Template Matching for RDF graphs S Qiao, ZM Ozsoyoglu arXiv preprint arXiv:1501.07184, 2015 | 4 | 2015 |
| Pipemizer: an optimizer for analytics data pipelines S Gakhar, J Cahoon, W Le, X Li, K Ravichandran, H Patel, M Friedman, ... Proceedings of the VLDB Endowment 15 (12), 3710-3713, 2022 | 3 | 2022 |
hiren patelProgram Manager, MicrosoftVerified email at microsoft.com
