31 May - 5 June 2026, The 2026 ACM Symposium on Principles of Database Systems (PODS 2026), Bengaluru, India
The PODS symposium series, held in conjunction with the SIGMOD conference series, provides a premier annual forum for the communication of new advances in the theoretical foundation of database systems. The PODS community aims to provide a solid scientific basis for methods, techniques, and solutions for the data management challenges that continually arise in our data-driven society. It develops new ways of advancing data management to reflect the rich landscape of data requirements in applications nowadays. Our goal is to develop solutions that ensure high levels of efficiency, scalability, usability, expressiveness, robustness, security, clarity, and privacy, among others. The PODS community is an open space where researchers from various areas related to the principles of computer science can discuss, interact, and propose solutions to problems in data management.
PODS seeks scientific articles that present principled contributions to modeling, application and system building in the context of data management. Such articles might be based, among others, on establishing theoretical results, developing new concepts and frameworks that deserve further exploration, providing experimental work that sheds light on the scientific foundations of the discipline, or a rigorous analysis of important industry artifacts. At a time when computer science is increasingly data-centric, it is essential to promote an active exchange of tools and techniques between PODS and other communities focused on data management. PODS thus pays special attention to those papers that help in the urgent process of integrating data management techniques within broader computer science.
All submissions must be made to EasyChair. LaTex users must format their submission using the standard ACM "acmsmall" proceedings stylesheet. A submission can be up to 15 pages, not including references, plus unlimited space for references. PODS 2026 will use a lightweight double-anonymous reviewing process.