| Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML CIDR, 2020 | 37* | 2020 |
| Incorporating Super-Operators in Big-Data Query Optimizers J Leeka, K Rajan PVLDB, 2019 | 21 | 2019 |
| A formal graph model for RDF and its implementation V Nguyen, J Leeka, O Bodenreider, A Sheth arXiv preprint arXiv:1606.00480, 2016 | 17 | 2016 |
| INSTalytics: Cluster Filesystem Co-design for Big-data Analytics M Sivathanu, M Vuppalapati, BS Gulavani, K Rajan, J Leeka, J Mohan, ... ACM Transactions on Storage (TOS) 15 (4), 1-30, 2020 | 12 | 2020 |
| RQ-RDF-3X: going beyond triplestores J Leeka, S Bedathur 2014 IEEE 30th International Conference on Data Engineering Workshops, 263-268, 2014 | 11 | 2014 |
| Quark-X An Efficient Top-K Processing Framework for RDF Quad Stores J Leeka, S Bedathur, D Bera, M Atre Proceedings of the 25th ACM International on Conference on Information and …, 2016 | 8 | 2016 |
| Triou, Dexin Zhu, Lucky Katahanas, Chakrapani Bhat Talapady, et al. 2021. The cosmos big data platform at Microsoft: over a decade of progress and a decade to look forward C Power, H Patel, A Jindal, J Leeka, B Jenkins, M Rys Proceedings of the VLDB Endowment 14 (12), 3148-3161, 2021 | 6 | 2021 |
| Production Experiences from Computation Reuse at Microsoft A Jindal, S Qiao, H Patel, A Roy, J Leeka, B Haynes. EDBT, 2021 | 6 | 2021 |
| The Cosmos Big Data Platform at Microsoft: Over a Decade of Progress and a Decade to Look Forward C Power, H Patel, A Jindal, J Leeka, B Jenkins, M Rys, E Triou, D Zhu, ... VLDB, 2021 | 5 | 2021 |
| Wangchao Le, Xiangnan Li, Kaushik Ravichandran, Hiren Patel, Marc Friedman, Brandon Haynes, Shi Qiao, Alekh Jindal, and Jyoti Leeka.“Pipemizer: An Optimizer for Analytics Data … S Gakhar, J Cahoon Proceedings of the VLDB Endowment (PVLDB), 2022 | 4 | 2022 |
| Cloudy with high chance of DBMS: A 10-year prediction for Enterprise-Grade ML. arXiv e-prints, page A Agrawal, R Chatterjee, C Curino, A Floratou, N Gowdal, M Interlandi, ... arXiv preprint arXiv:1909.00084, 2019 | 4 | 2019 |
| Towards Building Autonomous Data Services on Azure Y Zhu, Y Tian, J Cahoon, S Krishnan, A Agarwal, R Alotaibi, ... Companion of the 2023 International Conference on Management of Data, 217-224, 2023 | 3 | 2023 |
| Unshackling Database Benchmarking from Synthetic Workloads P Negi, L Bindschaedler, M Alizadeh, T Kraska, J Leeka, A Gruenheid, ... 2023 IEEE 39th International Conference on Data Engineering (ICDE), 3659-3662, 2023 | 3 | 2023 |
| Pipemizer: An Optimizer for Analytics Data Pipelines S Gakhar, J Cahoon, W Le, X Li, K Ravichandran, H Patel, M Friedman, ... PVLDB, 2022 | 3 | 2022 |
| Query Optimizer as a Service: An Idea Whose Time Has Come A Jindal, J Leeka SIGMOD Record, 2022 | 3 | 2022 |
| Triou, Dexin Zhu, Lucky Katahanas, Chakrapani Bhat Talapady, Joshua Rowe, Fan Zhang, Rich Draves, Marc Friedman, Ivan Santa Maria Filho, and Amrish Kumar. 2021. The Cosmos Big … C Power, H Patel, A Jindal, J Leeka, B Jenkins, M Rys Proc. VLDB Endow 14 (12), 3148-3161, 2021 | 3 | 2021 |
| Proactive Resource Allocation Policy for Microsoft Azure Cognitive Search O Poppe, P Castro, W Lang, J Leeka ACM SIGMOD Record 52 (3), 41-48, 2023 | 2 | 2023 |
| STREAK: An efficient engine for processing top-k SPARQL queries with spatial filters J Leeka, S Bedathur, D Bera, S Lakshminarasimhan arXiv preprint arXiv:1710.07411, 2017 | 1 | 2017 |
| Sibyl: Forecasting Time-Evolving Query Workloads H Huang, T Siddiqui, R Alotaibi, C Curino, J Leeka, A Jindal, J Zhao, ... Proceedings of the ACM on Management of Data 2 (1), 1-27, 2024 | | 2024 |
| Query set optimization in a data analytics pipeline J Leeka, S Gakhar, HS Patel, MT Friedman, B Haynes, Q Shi, A Jindal US Patent 11,847,118, 2023 | | 2023 |
Alekh JindalCEO and Co-founder, Tursio Inc.在 tursio.ai 的电子邮件经过验证
