Call for Papers
IMPORTANT DATES:
- Submission deadline: 28 March 2025
- Author notification: 20 April 2025
- Camera-ready version: 17 May 2025
- Workshop day: 22 June 2025
All deadlines are 11:59PM PST.
Submission website: https://openreview.net/group?id=ACM.org/SIGMOD/2025/Workshop/NOVAS
ABOUT NOVAS:
The advent of transformer-based architectures has disrupted the current technological landscape. One key feature that enables the success of these models is the extensive access to data: first, they are pre-trained on terabytes of data thanks to self-supervision and scalable data processing; then, they can leverage large input contexts during inference thanks to advances in model sizes, GPU optimizations, and caching strategies.
Interesting research questions are consolidating around this novel paradigm of data processing:
How to design systems that process large, heterogeneous collections of data?
How to optimize data processing under time, economic, and performance constraints?
How can database management techniques be leveraged to optimize the storage and retrieval of data for large transformer-based models?
How can we integrate LLMs with traditional database systems to enhance data query performance and ensure reliable outputs?
The workshop name, NOVAS stands for Novel Optimizations for Visionary AI Systems: with our workshop, we aim to provide a platform that can help bridge the current perceived gap between "data management'' and "generative AI'' research. We are calling for work or early ideas which may be deemed innovative, controversial, or disruptive if considered from the perspective of more established research areas.
For any questions regarding the workshop please contact us at chairs@novasworkshop.org
TOPICS OF INTEREST:
Topics of particular interest for the workshop include, but are not limited to:
DB-inspired techniques to optimize caching, indexing, or inference in generative ML architectures.
Multi-modal embeddings and semantic question-answering over multiple modalities.
Declarative systems to compose AI agents and multi-agent systems for data processing.
Scheduling and sharing of large workloads for LLMs.
LLM-informed database design, configuration, and tuning.
Strategies to deploy LLM architectures for data processing, e.g., RAG, chain-of-thought reasoning.
Vector databases for embeddings in RAG systems.
Benchmarks for data processing tasks using LLMs.
Integration of LLMs with transactional/real-time analytics databases.
Techniques for efficiently serving instances of transformer models.
SUBMISSION GUIDELINES:
The workshop will accept regular and short papers. We welcome short papers that present exciting work in progress, dataset contributions, or visionary/outrageous ideas.
All papers have to be submitted in single anonymous format, and must be prepared in accordance with the ACM template available here. All submissions to the workshop must adhere to the diversity and inclusion writing guidelines from ACM.
The following are the page limits (excluding references):
Regular papers: 6 pages
Short papers: 4 pages
Submission page will be available soon.
All submissions (in PDF format) should be sent to OpenReview at https://openreview.net/group?id=ACM.org/SIGMOD/2025/Workshop/NOVAS
REVIEWING PROCESS:
Submissions will be single anonymous: authors cannot see reviewer names, but reviewers can see author names. We use OpenReview to host papers and the reviewing process will be public. This means that reviewers' comments that can be seen by all, although the reviewers' identity will remain anonymous.
Conflicts of Interests (COIs) are handled using the same rules of SIGMOD 2025.
The use of LLMs is allowed as a general-purpose assist tool. Authors and reviewers should understand that they take full responsibility for the contents written under their name, including content generated by LLMs that could be construed as plagiarism or scientific misconduct (e.g., fabrication of facts). LLMs are not eligible for authorship.