Large language models (LLMs) have garnered significant interest due to their impressive capabilities on a wide range of tasks. However, integrating LLM-powered agents in applications that operate over complex data ecosystems poses significant challenges related to heterogeneous data management and discovery while balancing trade-offs involving cost, latency, accuracy, interpretability, and trustworthiness. Within such ecosystems, dubbed compound AI systems, agentic workflows often deal with a wide variety of information, such as proprietary data in an enterprise, multilingual low-resource data, and heterogeneous data types and formats, among others. Therefore, a robust integration of LLM agents in real-world applications necessitates a systems approach to tackle these challenges and ensure effective and efficient utilization of heterogeneous data. This workshop will focus on exploring innovative approaches towards building such data-aware compound AI systems in the era of LLMs while balancing objectives such as cost, efficiency, robustness, and interpretability. It will be a full-day workshop involving invited talks representative of the work done in these communities, research presentations, and a panel discussion exploring the design space of compound AI systems.
| Time | Event |
|---|---|
| 09:00 - 09:10 | Welcome Address |
| 09:10 - 10:00 | Keynote 1 (Ziawasch Abedjan) |
| 10:00 - 10:30 | Coffee Break |
| 10:30 - 12:00 | Long Paper Session |
| 12:00 - 13:00 | Lunch |
| 13:00 - 14:00 | Keynote 2 (Eser Kandogan) |
| 14:00 - 14:50 | Short Paper Session |
| 14:50 - 15:15 | Coffee Break |
| 15:15 - 16:30 | Panel Discussion |
| 16:30 - 17:00 | Awards and Closing Remarks |
We encourage participation from academic and industry researchers as well as practitioners in data management, AI, and, systems community and aim to foster interdisciplinary collaborations. We welcome work that proposes innovations in designing compound AI systems and their components as well as work that evaluates components of such systems or studies empirically how humans interact with these systems. We encourage research that comes from academic and industry researchers as well as practitioners in data management, AI, and systems community. A sample of topics that are in the spirit of this workshop include, but are not limited to are given below.
Submissions should present original results and substantial new work not currently under review or published elsewhere. The following submissions are accepted:
The page limit for both tracks is excluding the references. No appendix is allowed. Papers must be submitted via Microsoft CMT. Only electronic submissions in PDF format will be considered. Manuscripts must be prepared following the same rules as ICDE conference papers, i.e., in accordance with the IEEE format available here.
A paper submitted to DAIS cannot be under review for any other conference or journal. All accepted papers will appear in the conference proceedings. All accepted papers are expected to be presented at the workshop, and at least one author is required to register.
DAIS will follow a double-anonymous review process to evaluate submissions on the basis of relevance, originality, presentation quality, and technical contribution. The review process will be coordinated by the PC Chairs resulting in a final decision to either accept or reject the submission.