PODS Mission

The PODS community aims to provide a solid scientific basis for methods, techniques and solutions for the data management problems that continually arise in our data-driven society. Our goal is to develop solutions that ensure a high level of efficiency, scalability, expressiveness, robustness, flexibility, security, and privacy, among others. The PODS community has a pivotal position in computer science research. It develops new ways of advancing data management that reflect the rich landscape of today’s data driven applications. PODS thus welcomes contributions that help to expand the breadth and the depth of the data management toolbox. The PODS community is an open space in which researchers from various areas related to the principles of computer science can discuss, interact, and propose solutions to pressing data management problems.

Scope

PODS seeks scientific articles that present principled contributions to modeling, application, system building, and both theoretical and experimental validation 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 both widely used and recently developed 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 design: Data models, query languages, schemas, constraints
    Database access: Data structures, access methods, concurrency, transactions
    Data quality: Data cleaning, data discovery, data exploration
    Database processing: Query evaluation, query optimization, schema management, distributed data processing, approximate data processing
    Data analysis: Data mining, machine learning, information extraction, data streams
    Uncertainty: Incompleteness, inconsistency, ontological query answering, semi-structured 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