Neo: A learned query optimizer R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ... PVLDB 12 (11), 1705-1718,, 2019 | 252 | 2019 |
Deep reinforcement learning for join order enumeration R Marcus, O Papaemmanouil aiDM'18 Proceedings of the First International Workshop on Exploiting …, 2018 | 193 | 2018 |
Plan-structured deep neural network models for query performance prediction R Marcus, O Papaemmanouil PVLDB 12 (11), 1733–1746, 2019 | 101 | 2019 |
Bao: Making learned query optimization practical R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska ACM SIGMOD Record 51 (1), 6-13, 2022 | 98* | 2022 |
RadixSpline: a single-pass learned index A Kipf, R Marcus, A van Renen, M Stoian, A Kemper, T Kraska, ... Proceedings of the third international workshop on exploiting artificial …, 2020 | 95 | 2020 |
AI Meets AI: Leveraging Query Executions to Improve Index Recommendations B Ding, S Das, R Marcus, W Wu, S Chaudhuri, VR Narasayya 2019 International Conference on Management of Data (SIGMOD ’19), 2019 | 78 | 2019 |
Benchmarking learned indexes R Marcus, A Kipf, A van Renen, M Stoian, S Misra, A Kemper, T Neumann, ... PVLDB 14 (1), 1-13, 2021 | 76 | 2021 |
Towards a Hands-Free Query Optimizer through Deep Learning R Marcus, O Papaemmanouil CIDR 2019, 9th Biennial Conference on Innovative Data Systems Research, 2019 | 76 | 2019 |
Park: An open platform for learning augmented computer systems H Mao, P Negi, A Narayan, H Wang, J Yang, H Wang, R Marcus, ... NeurIPS 2019 32, 2019 | 63 | 2019 |
ARDA: automatic relational data augmentation for machine learning N Chepurko, R Marcus, E Zgraggen, RC Fernandez, T Kraska, D Karger PVLDB 13 (9), 2020 | 50 | 2020 |
SOSD: A benchmark for learned indexes A Kipf, R Marcus, A van Renen, M Stoian, A Kemper, T Kraska, ... arXiv preprint arXiv:1911.13014, 2019 | 50 | 2019 |
WiSeDB: a learning-based workload management advisor for cloud databases R Marcus, O Papaemmanouil PVLDB 9 (10), 780-791, 2016 | 46 | 2016 |
Cdfshop: Exploring and optimizing learned index structures R Marcus, E Zhang, T Kraska Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 39 | 2020 |
NashDB: An End-to-End Economic Method for Elastic Database Fragmentation, Replication, and Provisioning R Marcus, O Papaemmanouil, S Semenova, S Garber 2018 International Conference on Management of Data (SIGMOD ’18), 2018 | 32* | 2018 |
Cost-guided cardinality estimation: Focus where it matters P Negi, R Marcus, H Mao, N Tatbul, T Kraska, M Alizadeh 2020 IEEE 36th International Conference on Data Engineering Workshops (ICDEW …, 2020 | 28 | 2020 |
Releasing Cloud Databases from the Chains of Performance Prediction Models R Marcus, O Papaemmanouil CIDR 2017, 8th Biennial Conference on Innovative Data Systems Research, 2017 | 25 | 2017 |
Flow-Loss: Learning Cardinality Estimates That Matter P Negi, R Marcus, A Kipf, A van Renen, M Stoia, S Misra, A Kemper, ... Proceedings of the VLDB Endowment 14 (11), 2021 | 24 | 2021 |
Steering query optimizers: A practical take on big data workloads P Negi, M Interlandi, R Marcus, M Alizadeh, T Kraska, M Friedman, ... Proceedings of the 2021 International Conference on Management of Data, 2557 …, 2021 | 18 | 2021 |
Misim: An end-to-end neural code similarity system F Ye, S Zhou, A Venkat, R Marucs, N Tatbul, JJ Tithi, P Petersen, ... arXiv preprint arXiv:2006.05265, 2020 | 14 | 2020 |
A learning-based service for cost and performance management of cloud databases (demo) R Marcus, S Semenova, O Papaemmanouil 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 1361-1362, 2017 | 14 | 2017 |