ACM SIGMOD-SIGACT-SIGART Conference on Principles of Database Systems

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The annual ACM SIGMOD/PODS Conference is a leading international forum for database researchers, practitioners, developers, and users to explore cutting-edge ideas and results, and to exchange techniques, tools, and experiences. The conference includes a technical program with research and industrial talks, tutorials, demos, and focused workshops. PODS (Principles of Database Systems) is part of this joint conference. Topics of interest include but are not limited to: Data Management Systems, Monitoring, testing, and tuning database systems, Cloud, distributed, decentralized, and parallel data management, Database systems on emerging hardware, Embedded databases, IoT, and Sensor networks, Storage, indexing, and physical database design, Query processing and optimization, Transaction processing, Data warehousing, OLAP, Analytics, Data Models and Languages, Data models and semantics, Declarative programming languages and optimization, Spatial and temporal data management, Graphs, social networks, web data, and semantic web, Multimedia and information retrieval, Data lakes, Uncertain, probabilistic, and approximate databases, Streams and complex event processing, Human-Centric Data Management, Data exploration, visualization, query languages, and user interfaces, User-centric and human-in-the-loop data management, Crowdsourced and collaborative data management, Data Governance, Quality, and Fairness, Data integration, information extraction, and schema matching, Data provenance and workflows, Metadata management, Data security, privacy, and access control, Data quality and data cleaning, Responsible data management and data fairness, Modern AI & Data Management, Structured queries over unstructured data, Natural language queries, Machine learning methods for database engine internals, Machine learning methods for database engineering, Data management and metadata for machine learning pipelines, AI & knowledge bases, Data mining & prescriptive analytics in databases, Systems for AI.

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