First Workshop on Novel Optimizations for Visionary AI Systems (NOVAS)
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3722212.3724493.pdf
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795.28 KB
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Author(s)
Vitagliano, Gerardo
Liu, Chunwei
Cao, Lei
Sun, Huan
Papotti, Paolo
Date Issued
June 22, 2025
Publisher
ACM|Companion of the 2025 International Conference on Management of Data
Citation
Gerardo Vitagliano, Chunwei Liu, Lei Cao, Huan Sun, and Paolo Papotti. 2025. First Workshop on Novel Optimizations for Visionary AI Systems (NOVAS). In Companion of the 2025 International Conference on Management of Data (SIGMOD/PODS '25). Association for Computing Machinery, New York, NY, USA, 886–887.
Version
Final published version
Abstract
The first NOVAS workshop (Novel Optimizations for Visionary AI Systems) is aimed at hosting novel work at the intersection between artificial intelligence and data management. This area has emerged with the rise of transformer-based architectures, which have revolutionized data processing across modalities. While these models benefit from massive pre-training and large-context inference, there are significant challenges related to scalability, determinism, and resource constraints. These issues-long studied in the data management community-have sparked a convergence between generative AI and traditional database research.
The workshop will be held on June 22nd, in conjunction with SIGMOD/PODS 2025. The workshop solicits regular and short papers on topics including hardware and execution optimizations, high-level programming abstractions, integration of LLMs with relational databases, and new transformer architectures for structured data. By bridging together the different communities of machine learning, data systems, and information retrieval, NOVAS aims at becoming the venue to discuss, share ideas and early results, and spark new research collaborations for the next-generation of data-driven AI systems.
Description
SIGMOD-Companion ’25, Berlin, Germany
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1145/3722212.3724493
