Scientific workflow design for mere mortals

McPhillips, Timothy M.; Bowers, Shawn; Zinn, Daniel; Ludäscher, Bertram

doi:10.1016/j.future.2008.06.013

Cited by 143 publications

(102 citation statements)

References 31 publications

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“…-A dataflow GUI. A dataflow, or scientific workflow, is a form of domain-specific language (Johnston et al 2004;McPhillips et al 2009). Many such systems are visual programming languages and they are particularly convenient; they allow specification of programs by connecting icons in a GUI.…”

Section: Optional Featuresmentioning

confidence: 99%

On the analysis and design of software for reinforcement learning, with a survey of existing systems

Kovacs

Egginton

2011

Mach Learn

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Reinforcement Learning (RL) is a very complex domain and software for RL is correspondingly complex. We analyse the scope, requirements, and potential for RL software, discuss relevant design issues, survey existing software, and make recommendations for designers. We argue that broad and flexible libraries of reusable software components are valuable from a scientific, as well as practical, perspective, as they allow precise control over experimental conditions, encourage comparison of alternative methods, and allow a fuller exploration of the RL domain.

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Section: Optional Featuresmentioning

confidence: 99%

On the analysis and design of software for reinforcement learning, with a survey of existing systems

Kovacs

Egginton

2011

Mach Learn

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“…Recent work about scientific workflow design has demonstrated that constructing scientific workflows using an XML-like data model with XPath-like configurations leads to robust workflows with less shims and wires compared to approaches that do not deploy structured data models [10,13,12]. The key insight is that the XML data structure provides a level of indirection for actor communications and thus e«ectively removes the tight coupling between data flow, control flow, and the workflow graph.…”

Section: Motivationmentioning

confidence: 99%

Abstract Provenance Graphs: Anticipating and Exploiting Schema-Level Data Provenance

Zinn

Ludäscher

2010

Lecture Notes in Computer Science

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Abstract. Provenance graphs capture flow and dependency information recorded during scientific workflow runs, which can be used subsequently to interpret, validate, and debug workflow results. In this paper, we propose the new concept of Abstract Provenance Graphs (APGs). APGs are created via static analysis of a configured workflow W and input data schema, i.e., before W is actually executed. They summarize all possible provenance graphs the workflow W can create with input data of type , that is, for each input v ¾ there exists a graph homomorphism À v between the concrete and abstract provenance graph. APGs are helpful during workflow construction since (1) they make certain workflow design-bugs (e.g., selecting none or wrong input data for the actors) easy to spot; and (2) show the evolution of the overall data organization of a workflow. Moreover, after workflows have been run, APGs can be used to validate concrete provenance graphs. A more detailed version of this work is available as [14]. 1

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“…Most design interfaces allow the user to drag and drop tasks onto the workflow pane and to connect them by means of a few mouse clicks. Still, designing a good workflow can be hard; see [23] for a more theoretical overview of design issues.…”

Section: Designmentioning

confidence: 99%

e-BioFlow: Improving Practical Use of Workflow Systems in Bioinformatics

Wassink

Ooms

Neerincx

et al. 2010

Information Technology in Bio- And Medical Informatics, ITBAM 2010

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Abstract. Workflow management systems (WfMSs) are useful tools for bioinformaticians. As experiences with using WfMSs accumulate, shortcomings of current systems become apparent. In this paper, we focus on practical issues that hinder WfMS users and that arise in the design and execution of workflows, and in access of web services. We present e-BioFlow, a workflow engine that demonstrates in which way a number of these problems can be solved. e-BioFlow offers an improved user interface, can deal with large data volumes, stores all provenance, and has a powerful provenance browser. e-BioFlow also offers the possibility to design and run workflows step by step, allowing its users an explorative research style.

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Scientific workflow design for mere mortals

Cited by 143 publications

References 31 publications

On the analysis and design of software for reinforcement learning, with a survey of existing systems

On the analysis and design of software for reinforcement learning, with a survey of existing systems

Abstract Provenance Graphs: Anticipating and Exploiting Schema-Level Data Provenance

e-BioFlow: Improving Practical Use of Workflow Systems in Bioinformatics

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