1.

Record Nr.

UNINA9910731404503321

Autore

Geerts Floris

Titolo

PODS '23 : Proceedings of the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems / / Floris Geerts, Hung Q. Ngo, Stavros Sintos

Pubbl/distr/stampa

New York, NY : , : Association for Computing Machinery, , 2023

Descrizione fisica

1 online resource (392 pages)

Disciplina

005.7565

Soggetti

Database management

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Sommario/riassunto

It is our great pleasure to welcome you to the 42nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS 2023), held in Seattle, Washington, U.S.A., on June 18th - June 21st, 2023. As usual, PODS is held in conjunction with its sister conference, the 2023 ACM SIGMOD International Conference on Management of Data. Since the first edition of the symposium in 1982, PODS provides a premier annual forum for the communication of new advances in the theoretical foundations of data management. 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. In addition, 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. PODS papers meet very high-quality standards and are distinguished by a rigorous approach to widely diverse problems in data management, often bringing to bear techniques from a variety of different areas, including computational logic, finite model theory, computational complexity, algorithm design and analysis, programming languages, privacy, statistical theory, and artificial intelligence. In particular, the



papers included in this volume present principled contributions to modeling, application, system building, and both theoretical and experimental validation in the context of data management.