Workflows and e-Science: An overview of workflow system features and capabilities

Deelman, Ewa; Gannon, Dennis; Shields, Matt; Taylor, Ian

doi:10.1016/j.future.2008.06.012

Cited by 720 publications

(527 citation statements)

References 26 publications

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“…Our experiences complement those of [2] and [4]. To demonstrate that many of the problems we found can be solved, we designed and built our own workflow system called e-BioFlow [9].…”

Section: Introductionmentioning

confidence: 87%

“…Taverna has been developed in the course of the UK myGridproject and Kepler and Triana have originally been designed for use on grids for heavy scientific (such as astronomical, meteorological, or geological) computations. Kepler and Triana can handle massive workloads, which are several orders of magnitude larger than what is now common in bioinformatics [2]. Although originating from a Grid project, Taverna has focussed on applications in the bioinformatics domain.…”

Section: Workflow Use In Other Domainsmentioning

confidence: 99%

“…Today, workflow management systems (WfMSs) are recognised as useful tools for chaining computational tasks [1,2] and in particular for orchestrating web services [3,4]. Open-source WfMSs for scientific computation (e.g., Kepler [5] and Triana [6]) and specifically for bioinformatics (e.g., Taverna [7]) enjoy worldwide use.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Our experiences complement those of [2] and [4]. To demonstrate that many of the problems we found can be solved, we designed and built our own workflow system called e-BioFlow [9].…”

Section: Introductionmentioning

confidence: 87%

Section: Workflow Use In Other Domainsmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Furthermore, user's fidelity selection may not directly match a configuration and the nearest approximation may be required. Existing composition approaches do not provide a good mechanism to support such fidelity and timeliness trade-offs [8].…”

Section: Problemmentioning

confidence: 99%

Model-Based Assistance for Making Time/Fidelity Trade-Offs in Component Compositions

Dwivedi

Garlan

Schmerl

2014

2014 11th International Conference on Information Technology: New Generations

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Abstract-In many scientific fields, simulations and analyses require compositions of computational entities such as webservices, programs, and applications. In such fields, users may want various trade-offs between different qualities. Examples include: (i) performing a quick approximation vs. an accurate, but slower, experiment, (ii) using local slower execution environments vs. remote, but advanced, computing facilities, (iii) using quicker approximation algorithms vs. computationally expensive algorithms with smaller data. However, such trade-offs are difficult to make as many such decisions today are either (a) wired into a fixed configuration and cannot be changed, or (b) require detailed systems knowledge and experimentation to determine what configuration to use. In this paper we propose an approach that uses architectural models coupled with automated design space generation for making fidelity and timeliness trade-offs. We illustrate this approach through an example in the intelligence analysis domain.

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“…These simulations depend on the use of computational mechanisms that may be very complex. Scientific workflows are an abstraction that allows the specification of those experiments in a structured manner using a flow of programs, services and data to produce a final result (Deelman et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Similarity-based workflow clustering

Silva¹,

Chirigati

Maia³

et al. 2011

JCIS

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Scientists have been using scientific workflow management systems (SWfMS) to support scientific experiments. However, SWfMS expect a modeled workflow to be represented on its workflow language to be executed. The scientist does not have an assistance or guidance to obtain a modeled workflow. Experiment lines, which are a novel approach to deal with these limitations, allow for the abstract representation and systematic composition of experiments. Since there are many scientific workflows already modeled and successfully executed, they can be used to leverage the construction of new abstract representations. These previous experiments can be helpful by identifying scientific workflow clusters that are generated according to similarity criteria. This paper proposes SimiFlow, which is an architecture for similarity-based comparison and clustering to build experiment lines following a bottom-up approach.

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Workflows and e-Science: An overview of workflow system features and capabilities

Cited by 720 publications

References 26 publications

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

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

Model-Based Assistance for Making Time/Fidelity Trade-Offs in Component Compositions

Similarity-based workflow clustering

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