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Saehan Jo
Saehan Jo
Ph.D. Student in Computer Science, Cornell University
Dirección de correo verificada de cornell.edu
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Skinnerdb: Regret-bounded query evaluation via reinforcement learning
I Trummer, J Wang, Z Wei, D Maram, S Moseley, S Jo, J Antonakakis, ...
ACM Transactions on Database Systems (TODS) 46 (3), 1-45, 2021
1002021
Skinnerdb: regret-bounded query evaluation via reinforcement learning
I Trummer, S Moseley, D Maram, S Jo, J Antonakakis
Proceedings of the VLDB Endowment 11 (12), 2074-2077, 2018
452018
Verifying text summaries of relational data sets
S Jo, I Trummer, W Yu, X Wang, C Yu, D Liu, N Mehta
Proceedings of the 2019 International Conference on Management of Data, 299-316, 2019
30*2019
Mining an" anti-knowledge base" from Wikipedia updates with applications to fact checking and beyond
G Karagiannis, I Trummer, S Jo, S Khandelwal, X Wang, C Yu
Proceedings of the VLDB Endowment 13 (4), 561-573, 2019
212019
Supervised belief propagation: Scalable supervised inference on attributed networks
J Yoo, S Jo, U Kang
2017 IEEE International Conference on Data Mining (ICDM), 595-604, 2017
202017
Fast and scalable distributed loopy belief propagation on real-world graphs
S Jo, J Yoo, U Kang
Proceedings of the Eleventh ACM International Conference on Web Search and …, 2018
172018
Aggchecker: A fact-checking system for text summaries of relational data sets
S Jo, I Trummer, W Yu, X Wang, C Yu, D Liu, N Mehta
Proceedings of the VLDB Endowment 12 (12), 1938-1941, 2019
112019
Isum: Efficiently compressing large and complex workloads for scalable index tuning
T Siddiqui, S Jo, W Wu, C Wang, V Narasayya, S Chaudhuri
Proceedings of the 2022 International Conference on Management of Data, 660-673, 2022
102022
Demonstration of ThalamusDB: Answering Complex SQL Queries with Natural Language Predicates on Multi-Modal Data
S Jo, I Trummer
Companion of the 2023 International Conference on Management of Data, 179-182, 2023
42023
BitGourmet: Deterministic Approximation via Optimized Bit Selection
S Jo, I Trummer
The Conference on Innovative Data Systems Research (CIDR), 2020
42020
Demonstration of ScroogeDB: Getting more bang for the buck with deterministic approximation in the cloud
S Jo, J Pei, I Trummer
Proceedings of the VLDB Endowment 13 (12), 2961-2964, 2020
22020
SMART: Automatically Scaling Down Language Models with Accuracy Guarantees for Reduced Processing Fees
S Jo, I Trummer
arXiv preprint arXiv:2403.13835, 2024
2024
Compressing workloads for scalable index tuning
TA Siddiqui, JO Saehan, W Wu, C Wang, VR Narasayya, S Chaudhuri
US Patent App. 17/740,660, 2023
2023
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning (Extended Version)
T Siddiqui, S Jo, W Wu, C Wang, V Narasayya, S Chaudhuri
2022
Demonstration of BitGourmet: Data Analysis via Deterministic Approximation
S Jo, I Trummer
Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020
2020
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Artículos 1–15