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 |
|---|---|
| 09:15 - 09:30 | Welcome Address
Session chair: Sajjadur Rahman |
| 09:30 - 10:30 | Keynote 1
Session chair: Anna Fariha Speaker: Eser Kandogan Title: Agentic for Enterprise: Challenges and Opportunities in the 'Wild' [Slides] Abstract: With advances in AI and LLMs, in particular 'agentic computing', offering novel experiences and unprecedented capabilities, companies are increasingly eager to embrace the agentic AI to streamline their businesses, improve customer experiences, and explore novel business models. Yet, the road to 'agentic' isn't so straightforward, especially for enterprise companies. In this talk, I will present some challenges and opportunities of 'agentic for enterprise', both from the perspective of a developer of such agentic applications as well as a developer of agentic frameworks. The main thesis of the talk will be: as computation and data moves from the 'controlled' zone of determinism, structured, and apriori into the 'wild' zone of probabilistic, unstructured, and in-situ, we need to rethink the whole software stack, how we develop and deploy applications, how we acquire and process data, and how we present and interface to the user. I will make the case by discussing examples, identify design requirements for 'agentic for enterprise', and put out a few research opportunities for the data management, artificial intelligence, systems, and human-computer interaction research communities to tackle. |
| 10:30 - 11:00 | Coffee Break |
| 11:00 - 11:30 | Invited Paper Session
Session chair: Sairam Gurajada |
| 11:30 - 12:30 | Keynote 2
Session chair: Nikita Bhutani Speaker: Ziawasch Abedjan Title: Navigating Disruption: The Impact of AI Technologies on Data Integration Research [Slides] Abstract: The advent of novel AI technologies, such as foundation models and agentic systems, has introduced major disruptions in the field of data management, particularly in data integration. This talk will share our experiences in advancing data integration and preparation solutions, highlighting cases where these technologies have brought significant disruption. I will explore the trade-offs associated with leveraging such powerful tools and shed light on the new pitfalls that make data integration research increasingly challenging. By addressing both opportunities and pitfalls, this talk aims to provide insights into navigating this rapidly evolving landscape. |
| 12:30 - 14:00 | Lunch |
| 14:00 - 14:45 | Long Paper Session
Session chair: Arpit Narechania |
| 14:45 - 15:30 | Short Paper Session
Session chair: Kaustubh Beedkar |
| 15:30 - 16:00 | Coffee Break |
| 16:00 - 17:00 | Panel Discussion: Towards Scalable, Responsible, and Reliable Application of Agentic Systems
Moderator: Sajjadur Rahman Panelists: Ziawasch Abedjan, Kaustubh Beedkar, Estevam Hruschka, and Arpit Narechania |
| 17:00 - 17:15 | Closing Remarks
Speaker: Sajjadur Rahman |
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.
