- 2024
- Jan-Micha Bodensohn
, Carsten Binnig:
Rethinking Table Retrieval from Data Lakes. aiDM@SIGMOD 2024: 2:1-2:5 - Jenny Gao, Jialin Ding, Sivaprasad Sudhir, Samuel Madden:
Learning Bit Allocations for Z-Order Layouts in Analytic Data Systems. aiDM@SIGMOD 2024: 5:1-5:9 - Hasan M. Jamil:
Smart Science Needs Linked Open Data with a Dash of Large Language Models and Extended Relations. aiDM@SIGMOD 2024: 1:1-1:11 - Amadou Latyr Ngom, Tim Kraska:
Mallet: SQL Dialect Translation with LLM Rule Generation. aiDM@SIGMOD 2024: 3:1-3:5 - Zixuan Yi, Yao Tian, Zachary G. Ives, Ryan Marcus:
Low Rank Approximation for Learned Query Optimization. aiDM@SIGMOD 2024: 4:1-4:5 - Proceedings of the Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2024, Santiago, Chile, 14 June 2024. ACM 2024 [contents]
- 2023
- Mohamed Abdelaal, Rashmi Koparde, Harald Schöning:
AutoCure: Automated Tabular Data Curation Technique for ML Pipelines. aiDM@SIGMOD 2023: 1:1-1:11 - Pratyush Agnihotri, Boris Koldehofe, Carsten Binnig, Manisha Luthra:
Zero-Shot Cost Models for Parallel Stream Processing. aiDM@SIGMOD 2023: 5:1-5:5 - Damjan Gjurovski, Sebastian Michel:
Tuple Bubbles: Learned Tuple Representations for Tunable Approximate Query Processing. aiDM@SIGMOD 2023: 2:1-2:9 - Zhitao Gong, Wenlu Wang:
Adversarial and Clean Data Are Not Twins. aiDM@SIGMOD 2023: 6:1-6:5 - Keizo Hori, Yuya Sasaki, Daichi Amagata, Yuki Murosaki, Makoto Onizuka:
Learned Spatial Data Partitioning. aiDM@SIGMOD 2023: 3:1-3:8 - Matthias Urban, Duc Dat Nguyen, Carsten Binnig:
OmniscientDB: A Large Language Model-Augmented DBMS That Knows What Other DBMSs Do Not Know. aiDM@SIGMOD 2023: 4:1-4:7 - Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, Donatella Firmani, Andreas Kipf:
Proceedings of the Sixth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM@SIGMOD 2023, Seattle, WA, USA, 18 June 2023. ACM 2023 [contents] - 2022
- Mikkel Møller Andersen, Pinar Tözün:
Micro-architectural analysis of a learned index. aiDM@SIGMOD 2022: 5:1-5:12 - Tamer Eldeeb, Zhengneng Chen, Asaf Cidon, Junfeng Yang:
Neuroshard: towards automatic multi-objective sharding with deep reinforcement learning. aiDM@SIGMOD 2022: 1:1-1:12 - Andreas Kipf, Dominik Horn, Pascal Pfeil, Ryan Marcus, Tim Kraska:
LSI: a learned secondary index structure. aiDM@SIGMOD 2022: 4:1-4:5 - Jin Wang, Yuliang Li, Wataru Hirota, Eser Kandogan:
Machop: an end-to-end generalized entity matching framework. aiDM@SIGMOD 2022: 2:1-2:10 - Michal Zwolak, Zainab Abbas, Sonia Horchidan, Paris Carbone, Vasiliki Kalavri:
GCNSplit: bounding the state of streaming graph partitioning. aiDM@SIGMOD 2022: 3:1-3:12 - Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, Donatella Firmani, Ryan Marcus:
aiDM '22: Proceedings of the Fifth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, Philadelphia, Pennsylvania, USA, 17 June 2022. ACM 2022, ISBN 978-1-4503-9377-5 [contents] - 2021
- Michael Günther, Maik Thiele, Julius Gonsior, Wolfgang Lehner:
Pre-Trained Web Table Embeddings for Table Discovery. aiDM@SIGMOD 2021: 24-31 - Lujing Cen, Andreas Kipf, Ryan Marcus, Tim Kraska:
LEA: A Learned Encoding Advisor for Column Stores. aiDM@SIGMOD 2021: 32-35 - Martin Eppert, Philipp Fent, Thomas Neumann:
A Tailored Regression for Learned Indexes: Logarithmic Error Regression. aiDM@SIGMOD 2021: 9-15 - Mayank Mishra, Rekha Singhal:
RUSLI: Real-time Updatable Spline Learned Index. aiDM@SIGMOD 2021: 1-8 - Aurélien Personnaz, Sihem Amer-Yahia, Laure Berti-Équille, Maximilian Fabricius, Srividya Subramanian:
Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning. aiDM@SIGMOD 2021: 16-23 - Mohan Zhang, Oliver Schulte, Yudong Luo:
Leveraging Approximate Constraints for Localized Data Error Detection. aiDM@SIGMOD 2021: 36-44 - Rajesh Bordawekar, Yael Amsterdamer, Oded Shmueli, Nesime Tatbul:
aiDM '21: Fourth Workshop in Exploiting AI Techniques for Data Management, Virtual Event, China, 25 June, 2021. ACM 2021, ISBN 978-1-4503-8535-0 [contents] - 2020
- Ahmed S. Abdelhamid, Walid G. Aref:
PartLy: learning data partitioning for distributed data stream processing. aiDM@SIGMOD 2020: 6:1-6:4 - Zhiwei Fan, Rathijit Sen, Paraschos Koutris, Aws Albarghouthi:
Automated tuning of query degree of parallelism via machine learning. aiDM@SIGMOD 2020: 2:1-2:4 - Vahid Ghadakchi, Mian Xie, Arash Termehchy:
Bandit join: preliminary results. aiDM@SIGMOD 2020: 1:1-1:4 - Runsheng Benson Guo, Khuzaima Daudjee:
Research challenges in deep reinforcement learning-based join query optimization. aiDM@SIGMOD 2020: 3:1-3:6
