Parimarjan Negi

According to our database, Parimarjan Negi authored at least 15 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Machine Learning for Out of Distribution Database Workloads

Parimarjan Negi

PhD thesis, 2024

Stage: Query Execution Time Prediction in Amazon Redshift.

Ziniu Wu, Ryan Marcus, Zhengchun Liu, Parimarjan Negi, Vikram Nathan, Pascal Pfeil, Gaurav Saxena, Mohammad Rahman, Balakrishnan Narayanaswamy, Tim Kraska

Proceedings of the Companion of the 2024 International Conference on Management of Data, 2024

2023

Robust Query Driven Cardinality Estimation under Changing Workloads.

Parimarjan Negi, Ziniu Wu, Andreas Kipf, Nesime Tatbul, Ryan Marcus, Sam Madden, Tim Kraska, Mohammad Alizadeh

Proc. VLDB Endow., 2023

FactorJoin: A New Cardinality Estimation Framework for Join Queries.

Ziniu Wu, Parimarjan Negi, Mohammad Alizadeh, Tim Kraska, Samuel Madden

Proc. ACM Manag. Data, 2023

Unshackling Database Benchmarking from Synthetic Workloads.

Parimarjan Negi, Laurent Bindschaedler, Mohammad Alizadeh, Tim Kraska, Jyoti Leeka, Anja Gruenheid, Matteo Interlandi

Proceedings of the 39th IEEE International Conference on Data Engineering, 2023

2022

Bao: Making Learned Query Optimization Practical.

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska

SIGMOD Rec., 2022

2021

Flow-Loss: Learning Cardinality Estimates That Matter.

Parimarjan Negi, Ryan Marcus, Andreas Kipf, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh

Proc. VLDB Endow., 2021

Steering Query Optimizers: A Practical Take on Big Data Workloads.

Parimarjan Negi, Matteo Interlandi, Ryan Marcus, Mohammad Alizadeh, Tim Kraska, Marc T. Friedman, Alekh Jindal

Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

2020

Bao: Learning to Steer Query Optimizers.

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, Tim Kraska

CoRR, 2020

High Throughput Cryptocurrency Routing in Payment Channel Networks.

Vibhaalakshmi Sivaraman, Shaileshh Bojja Venkatakrishnan, Kathleen Ruan, Parimarjan Negi, Lei Yang, Radhika Mittal, Giulia Fanti, Mohammad Alizadeh

Proceedings of the 17th USENIX Symposium on Networked Systems Design and Implementation, 2020

Cost-Guided Cardinality Estimation: Focus Where it Matters.

Parimarjan Negi, Ryan Marcus, Hongzi Mao, Nesime Tatbul, Tim Kraska, Mohammad Alizadeh

Proceedings of the 36th IEEE International Conference on Data Engineering Workshops, 2020

2019

Neo: A Learned Query Optimizer.

Ryan Marcus, Parimarjan Negi, Hongzi Mao, Chi Zhang, Mohammad Alizadeh, Tim Kraska, Olga Papaemmanouil, Nesime Tatbul

Proc. VLDB Endow., 2019

Park: An Open Platform for Learning-Augmented Computer Systems.

Hongzi Mao, Parimarjan Negi, Akshay Narayan, Hanrui Wang, Jiacheng Yang, Haonan Wang, Ryan Marcus, Ravichandra Addanki, Mehrdad Khani Shirkoohi, Songtao He, Vikram Nathan, Frank Cangialosi, Shaileshh Bojja Venkatakrishnan, Wei-Hung Weng, Song Han, Tim Kraska, Mohammad Alizadeh

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Evaluating End-to-End Optimization for Data Analytics Applications in Weld.

Shoumik Palkar, James Thomas, Deepak Narayanan, Pratiksha Thaker, Rahul Palamuttam, Parimarjan Negi, Anil Shanbhag, Malte Schwarzkopf, Holger Pirk, Saman P. Amarasinghe, Samuel Madden, Matei Zaharia

Proc. VLDB Endow., 2018

K-means++ vs. Behavioral Biometrics: One Loop to Rule Them All.

Parimarjan Negi, Prafull Sharma, Vivek Jain, Bahman Bahmani

Proceedings of the 25th Annual Network and Distributed System Security Symposium, 2018