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A knowledge-based approach to scientific workflow composition

McIver, Russell P. 2015. A knowledge-based approach to scientific workflow composition. PhD Thesis, Cardiff University.
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Abstract

Scientific Workflow Systems have been developed as a means to enable scientists to carry out complex analysis operations on local and remote data sources in order to achieve their research goals. Systems typically provide a large number of components and facilities to enable such analysis to be performed and have matured to a point where they offer many complex capabilities. This complexity makes it difficult for scientists working with these systems to readily achieve their goals. In this thesis we describe the increasing burden of knowledge required of these scientists in order for them to specify the outcomes they wish to achieve within the workflow systems. We consider ways in which the challenges presented by these systems can be reduced, focusing on the following questions: How can metadata describing the resources available assist users in composing workflows? Can automated assistance be provided to guide users through the composition process? Can such an approach be implemented so as to work with the resources provided by existing Scientific Workflow Systems? We have developed a new approach to workflow composition which makes use of a number of features: an ontology for recording metadata relating to workflow components, a set of algorithms for analyzing the state of a workflow composition and providing suggestions for how to progress based on this metadata, an API to enable both the algorithms and metadata to utilise the resources provided by existing Scientific Workflow Systems, and a prototype user interface to demonstrate how our proposed approach to workflow composition can work in practice. We evaluate the system to show the approach is valid and capable of reducing some of the difficulties presented by existing systems, but that limitations exist regarding the complexity of workflows which can be composed, and also regarding the challenge of initially populating the metadata ontology.

Item Type: Thesis (PhD)
Status: Unpublished
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Funders: Microsoft Research Europe Studentship
Date of First Compliant Deposit: 30 March 2016
Last Modified: 28 Jun 2019 03:24
URI: http://orca.cf.ac.uk/id/eprint/80633

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