Automating Workflow/Pipeline Design-Reference-Cited by-同舟云学术

Automating Workflow/Pipeline Design

Published:2022 Issue: Volume: Page:123-140
ISSN:1611-2482
Container-title:Metalearning
language:
Short-container-title:

Author:

Brazdil Pavel,van Rijn Jan N.,Soares Carlos,Vanschoren Joaquin

Abstract

SummaryThis chapter discusses the design of workflows (or pipelines), which represent solutions that involve more than one algorithm. This is motivated by the fact that many tasks require such solutions. This problem is non-trivial, as the number of possible workflows (and their configurations) can be rather large. This chapter discusses various methods that can be used to restrict the design options and thus reduce the size of the configuration space. These include, for instance, ontologies and context-free grammars. Each of these formalisms has its merits and shortcomings. Many platforms have resorted to planning systems that use operators. These can be designed to be in accordance with the given ontologies or grammars. As the search space may be rather large, it is important to leverage prior experience. This topic is addressed in one of the sections, which discusses rankings of plans that have proved to be useful in the past. The workflows/pipelines that have proved successful in the past can be retrieved and used as plans in future tasks. Thus, it is possible to exploit both planning and metalearning.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-67024-5_7

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