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Ryan Marcus
Ryan Marcus
MIT CSAIL
Verified email at csail.mit.edu - Homepage
Title
Cited by
Cited by
Year
Neo: A learned query optimizer
R Marcus, P Negi, H Mao, C Zhang, M Alizadeh, T Kraska, ...
PVLDB 12 (11), 1705-1718,, 2019
2022019
Deep reinforcement learning for join order enumeration
R Marcus, O Papaemmanouil
aiDM'18 Proceedings of the First International Workshop on Exploiting …, 2018
1622018
Plan-structured deep neural network models for query performance prediction
R Marcus, O Papaemmanouil
PVLDB 12 (11), 1733–1746, 2019
772019
Towards a Hands-Free Query Optimizer through Deep Learning
R Marcus, O Papaemmanouil
CIDR 2019, 9th Biennial Conference on Innovative Data Systems Research, 2019
672019
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
642019
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
622020
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
56*2022
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
552019
Benchmarking learned indexes
R Marcus, A Kipf, A van Renen, M Stoian, S Misra, A Kemper, T Neumann, ...
PVLDB 14 (1), 1-13, 2021
472021
WiSeDB: a learning-based workload management advisor for cloud databases
R Marcus, O Papaemmanouil
PVLDB 9 (10), 780-791, 2016
442016
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
352019
ARDA: automatic relational data augmentation for machine learning
N Chepurko, R Marcus, E Zgraggen, RC Fernandez, T Kraska, D Karger
PVLDB 13 (9), 2020
342020
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
282020
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
23*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
222020
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
222017
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
132021
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
112020
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
112017
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
102021
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