Co-located with ACM SIGMOD/PODS 2026

Recently, the field of Artificial Intelligence (AI) has been experiencing a resurgence. AI broadly covers a wide swath of techniques, which include logic-based approaches, probabilistic graphical models, machine learning approaches such as deep learning. Advances in specialized hardware capabilities (e.g., Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), Field-Programmable Gate Arrays (FPGAs), etc.), software ecosystem (e.g., programming languages such as Python, Data Science frameworks, and accelerated ML libraries), and systems infrastructure (e.g., cloud servers with AI accelerators) have led to wide-spread adoption of AI techniques in a variety of domains. Examples of such domains include image classification, autonomous driving, automatic speech recognition, and conversational systems (e.g., chatbots). AI solutions not only support multiple data types (e.g., images, speech, or text), but also are available in various configurations and settings, from personal devices to large-scale distributed systems.

Despite the widespread adoption of AI across diverse domains, its integration with data management systems remains in its infancy. Currently, most database management systems (DBMS) serve primarily as repositories for feeding input data to AI models and storing results. Recently, there has been increasing interest in using AI techniques within data management systems, including natural language interfaces to relational databases and machine learning techniques for query optimization and performance tuning. However, significant opportunities remain to harness the full potential of AI for enhancing data management workloads.

aiDM'26 is a one-day workshop that will bring together people from academia and industry to explore innovative ways to integrate AI techniques into data management systems. The workshop will focus on leveraging AI to enhance various components of data management systems, including user interfaces, tooling, performance optimizations, and support for new query types and workloads. Special attention will be given to transparently exploiting AI techniques, such as Generative AI frameworks, for enterprise-class data management workloads. We aim to identify key research areas and inspire new initiatives in this emerging and transformative field.

The goal of the workshop is to take a holistic view of various AI technologies and investigate how they can be applied to different component of an end-to-end data management pipeline. Special emphasis would be given to how AI techniques could be used for enhancing user experience by reducing complexity in tools, or providing newer insights, or providing better user interfaces. Topics of interest include, but are not restricted to:

• Integration into Agentic and Orchestration Frameworks
• Enabling different types of RAG Capabilities
• New AI-enabled business intelligence (BI) queries for relational databases
• Integration of Large Language Models with databases and supporting services (e.g., Generative AI)
• Supporting Large Reasoning Models
• Natural language queries and conversational interfaces
• AI-enabled database programming (e.g., natural language queries, SQL co-pilots, etc.)
• Design and Implementation of Vector Databases for unstructured data
• Ethics, governance, and societal implications of AI-enabled databases
• Reasoning over knowledge bases
• Self-tuning Databases using reinforced learning
• Impact of model interpretability
• Supporting multiple datatypes (e.g., images or time-series data)
• Supporting semi-structured, streaming, and graph databases
• Impact of AI on tooling, e.g., ETL or data cleaning
• Performance implications of AI-enabled queries
• AI-enabled databases for managing and supporting AI workloads
• AI strategies for data provenence, access control, anomaly detection and cyber security
• Case studies of AI-accelerated workloads
• AI-driven data compression and storage optimization

Workshop Steering Committee

• Rajesh Bordawekar, NVIDIA
• Oded Shmueli, Hirundo Ltd., and Emeritus Professor at Technion - Israel Institute of Technology

Workshop Program Chairs

• Kavitha Srinivas, IBM T. J. Watson Research Center
• Manisha Luthra Agnihotri, TU Darmstadt and DFKI
• Selim Tekin, Georgia Institute of Technology

Program Committee

• Anwesha Saha, Boston University
• Weiwei Gong, Oracle
• Jerry Liu, Columbia University
• Liane Vogel, TU Darmstadt
• Arijit Khan, Bowling Green
• Fatih Illhan, Georgia Institute of Technology
• Varun Pandey, Technische Universität Nürnberg
• Nils Strassenburg, HPI Potsdam
• Thaleia-Dimitra Doudali, IMDEA
• Xiao Li, ITU Copenhagen
• Maximilian Böther, ETH Zürich

Important Dates 

• Paper Submission: Monday, March 30, 2026
• Notification of Acceptance: Friday, 24th April, 2026
• Camera-ready Submission: Monday, 4th May, 2026

Submission Site 

All submissions will be handled electronically via EasyChair.

Formatting Guidelines 

We will use the same document templates as the SIGMOD/PODS conferences (the ACM format). Like SIGMOD/PODS'26, the aiDM submission should be double-blind.

It is the authors' responsibility to ensure that their submissions adhere strictly to the ACM format. In particular, it is not allowed to modify the format with the objective of squeezing in more material. Submissions that do not comply with the formatting detailed here will be rejected without review. 

The paper length for a full paper is limited upto 12 pages, with unlimited pages of references. However, shorter papers (4 or 8 pages) are encouraged as well.  

All accepted papers will be indexed via the ACM digital library and available for download from the workshop webpage in the digital library.