The Principles of Database Systems (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 foundations of data management, traditional or nontraditional (see https://databasetheory.org/PODS).

The 2025 conference will be held in Berlin, Germany.

Mission

The PODS community has a pivotal position in the research of computer science. It 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.

Scope

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.

Topics that fit the interests of the symposium include, but are not limited to:

    Database processing: Query evaluation, query optimization, schema management, distributed data processing, approximate data processing
    Database design: Data models, query languages, schemas, constraints
    Database access: Data structures, access methods, concurrency, transactions
    Data quality: Data consistency, data cleaning, data discovery, data exploration
    Data analysis: Data mining, machine learning, information extraction, data streams
    Uncertainty: Incomplete data, inconsistent data, probabilistic data
    Interoperability: Mappings and views, data integration, data exchange, ontology-based data access
    Responsible data management: Access control, privacy, security, verification, ethical aspects of data management
    Human-database interaction: data summarization, data visualization, explanation for database queries
    Connections between database principles and other disciplines