Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)
Authors: 
Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, 
Renata Borovica-Gajic, 
Donatella FirmaniAuthors Info & Claims
Rajesh Bordawekar, Oded Shmueli, Yael Amsterdamer, Renata Borovica-Gajic, Donatella FirmaniAuthors Info & ClaimsPages 649 - 650
Abstract
Recent advances in AI techniques, as well as enabling hardware and infrastructure, have led to the integration of AI across wide-ranging domains and tasks. In particular, AI has been used to handle various types of data (including numerical, textual and image data) and has been adopted in large-scale distributed systems. From a data management perspective, this calls for the harnessing of state-of-the-art AI solutions for data management tasks and systems. aiDM is a full-day workshop that offers a stage for innovative interdisciplinary research that studies the interaction between AI and data management and develops new AI technologies for data-related tasks. This year, aiDM'24 particularly focuses on the transparent exploitation of AI techniques (e.g., using Generative AI frameworks) for data management for enterprise class workloads.
References
[1]
Katrin Affolter, Kurt Stockinger, and Abraham Bernstein. 2019. A comparative survey of recent natural language interfaces for databases. VLDB J., Vol. 28, 5 (2019), 793--819.
[2]
Hai Lan, Zhifeng Bao, J. Shane Culpepper, and Renata Borovica-Gajic. 2023. Updatable Learned Indexes Meet Disk-Resident DBMS - From Evaluations to Design Choices. Proceedings of the ACM on Management of Data (SIGMOD), Vol. 1, 2 (2023), 139:1--139:22.
[3]
Ryan Marcus, Parimarjan Negi, Hongzi Mao, Nesime Tatbul, Mohammad Alizadeh, and Tim Kraska. 2021. Bao: Making Learned Query Optimization Practical. In SIGMOD. 1275--1288.
[4]
Khan Muhammad, Amin Ullah, Jaime Lloret, Javier Del Ser, and Victor Hugo C. de Albuquerque. 2021. Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions. IEEE Trans. Intell. Transp. Syst., Vol. 22, 7 (2021), 4316--4336.
[5]
R. Malinga Perera, Bastian Oetomo, Benjamin I. P. Rubinstein, and Renata Borovica-Gajic. 2023. No DBA? No Regret! Multi-Armed Bandits for Index Tuning of Analytical and HTAP Workloads With Provable Guarantees. IEEE Transactions on Knowledge and Data Engineering (TKDE), Vol. 35, 12 (2023), 12855--12872.
[6]
Yao Qin, Nicholas Carlini, Garrison W. Cottrell, Ian J. Goodfellow, and Colin Raffel. 2019. Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. In ICML. 5231--5240.
[7]
Waseem Rawat and Zenghui Wang. 2017. Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review. Neural Comput., Vol. 29, 9 (2017), 2352--2449.
[8]
Rui Yan. 2018. "Chitty-Chitty-Chat Bot": Deep Learning for Conversational AI. In IJCAI. 5520--5526.
Index Terms
- Seventh International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM)
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Information & Contributors
Information
Published In
June 2024
694 pages
ISBN:9798400704222
DOI:10.1145/3626246
- General Chairs:
- Pablo Barcelo,
- Nayat Sanchez-Pi,
- Program Chairs:
- Alexandra Meliou,
- S. Sudarshan
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 June 2024
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SIGMOD/PODS '24
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