@inproceedings{73031940e9764fb8a635d6c953b706a1,
title = "Managing Event Oriented Workflows",
abstract = "This paper introduces an event-driven solution for modern scientific workflows. This novel approach enables truly dynamic workflows by splitting them into their constituent parts, defined using combinations of Patterns and Recipes, and lacking any meaningful inter-dependencies. The theory behind this system is set out, and an example workflow is presented. A python package mig-meow, which implements this workflow system is also shown and explained. The use cases of various user groups are considered to asses the feasibility of the design, and it is found to be sufficient, especially in light of recent workflow requirements for dynamic looping, optional outputs and in-The-loop interactions. ",
keywords = "adaptive, dynamic, event driven, Jupyter, MiG, workflow",
author = "David Marchant and Rasmus Munk and Brenne, \{Elise O.\} and Brian Vinter",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 2nd IEEE/ACM Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing, XLOOP 2020 ; Conference date: 12-11-2020",
year = "2020",
month = nov,
doi = "10.1109/XLOOP51963.2020.00009",
language = "English",
series = "Proceedings of XLOOP 2020: 2nd Annual Workshop on Extreme-Scale Experiment-in-the-Loop Computing, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis",
pages = "23--28",
booktitle = "Proceedings of XLOOP 2020",
publisher = "IEEE",
}