Abstract

Data-driven application systems often depend on complex, data-intensive programs operating on distributed datasets that originate from a variety of scientific instruments and repositories to provide time-critical responses for observed phenomena in different areas of science, e.g., weather warning systems, seismology, and ocean sciences, among others. A major challenge for these observational science application systems is the integration of data into the scientist’s workflow and how these workflows could leverage advanced networking and distributed computational capabilities to analyze real-time data streams. In particular and, moreover, in the case of dynamic data-driven applications systems (DDDAS), such capabilities become even more imperative. In this chapter, we present the DyNamo network-centric platform that addresses some of the critical challenges faced by dynamic data-driven workflows …

Date

2023

Authors

George Papadimitriou, Cong Wang, Eric Lyons, Komal Thareja, Paul Ruth, JJ Villalobos, Ivan Rodero, Ewa Deelman, Michael Zink, Anirban Mandal

Book

Handbook of Dynamic Data Driven Applications Systems: Volume 2

Pages

835-868

Publisher

Springer International Publishing