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Scientific workflow system
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Scientific workflow system
A scientific workflow system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or workflow, in a scientific application. Scientific workflow systems are generally developed for use by scientists from different disciplines like astronomy, earth science, and bioinformatics. All such systems are based on an abstract representation of how a computation proceeds in the form of a directed graph, where each node represents a task to be executed and edges represent either data flow or execution dependencies between different tasks. Each system typically provides a visual front-end, allowing the user to build and modify complex applications with little or no programming expertise.
Distributed scientists can collaborate on conducting large scale scientific experiments and knowledge discovery applications using distributed systems of computing resources, data sets, and devices. Scientific workflow systems play an important role in enabling this vision.
More specialized scientific workflow systems provide a visual programming front end enabling users to easily construct their applications as a visual graph by connecting nodes together, and tools have also been developed to build such applications in a platform-independent manner. Each directed edge in the graph of a workflow typically represents a connection from the output of one application to the input of the next. A sequence of such edges may be called a pipeline.
The simplest computerized scientific workflows are scripts that call in data, programs, and other inputs and produce outputs that might include visualizations and analytical results. These may be implemented in programs such as R or MATLAB, using a scripting language such as Python with a command-line interface, or more recently using open-source web applications such as Jupyter Notebook.
There are many motives for differentiating scientific workflows from traditional business process workflows. These include:
By focusing on the scientists, the focus of designing scientific workflow system shifts away from the workflow scheduling activities, typically considered by grid computing environments for optimizing the execution of complex computations on predefined resources, to a domain-specific view of what data types, tools and distributed resources should be made available to the scientists and how can one make them easily accessible and with specific Quality of Service requirements
Scientific workflows are now recognized[by whom?] as a crucial element of the cyberinfrastructure, facilitating e-Science. Typically sitting on top of a middleware layer, scientific workflows are a means by which scientists can model, design, execute, debug, re-configure, and re-run their analysis and visualization pipelines. Part of the established scientific method is to create a record of the origins of a result, how it was obtained, experimental methods used, machine calibrations and parameters, etc. It is the same in e-Science, except provenance data are a record of the workflow activities invoked, services and databases accessed, data sets used, and so forth. Such information is useful for a scientist to interpret their workflow results and for other scientists to establish trust in the experimental result.
Social networking communities such as myExperiment have been developed to facilitate sharing and collaborative development of scientific workflows. Galaxy provide collaborative mechanisms for editing and publication of workflow definitions and workflow results directly on the Galaxy installation.
Hub AI
Scientific workflow system AI simulator
(@Scientific workflow system_simulator)
Scientific workflow system
A scientific workflow system is a specialized form of a workflow management system designed specifically to compose and execute a series of computational or data manipulation steps, or workflow, in a scientific application. Scientific workflow systems are generally developed for use by scientists from different disciplines like astronomy, earth science, and bioinformatics. All such systems are based on an abstract representation of how a computation proceeds in the form of a directed graph, where each node represents a task to be executed and edges represent either data flow or execution dependencies between different tasks. Each system typically provides a visual front-end, allowing the user to build and modify complex applications with little or no programming expertise.
Distributed scientists can collaborate on conducting large scale scientific experiments and knowledge discovery applications using distributed systems of computing resources, data sets, and devices. Scientific workflow systems play an important role in enabling this vision.
More specialized scientific workflow systems provide a visual programming front end enabling users to easily construct their applications as a visual graph by connecting nodes together, and tools have also been developed to build such applications in a platform-independent manner. Each directed edge in the graph of a workflow typically represents a connection from the output of one application to the input of the next. A sequence of such edges may be called a pipeline.
The simplest computerized scientific workflows are scripts that call in data, programs, and other inputs and produce outputs that might include visualizations and analytical results. These may be implemented in programs such as R or MATLAB, using a scripting language such as Python with a command-line interface, or more recently using open-source web applications such as Jupyter Notebook.
There are many motives for differentiating scientific workflows from traditional business process workflows. These include:
By focusing on the scientists, the focus of designing scientific workflow system shifts away from the workflow scheduling activities, typically considered by grid computing environments for optimizing the execution of complex computations on predefined resources, to a domain-specific view of what data types, tools and distributed resources should be made available to the scientists and how can one make them easily accessible and with specific Quality of Service requirements
Scientific workflows are now recognized[by whom?] as a crucial element of the cyberinfrastructure, facilitating e-Science. Typically sitting on top of a middleware layer, scientific workflows are a means by which scientists can model, design, execute, debug, re-configure, and re-run their analysis and visualization pipelines. Part of the established scientific method is to create a record of the origins of a result, how it was obtained, experimental methods used, machine calibrations and parameters, etc. It is the same in e-Science, except provenance data are a record of the workflow activities invoked, services and databases accessed, data sets used, and so forth. Such information is useful for a scientist to interpret their workflow results and for other scientists to establish trust in the experimental result.
Social networking communities such as myExperiment have been developed to facilitate sharing and collaborative development of scientific workflows. Galaxy provide collaborative mechanisms for editing and publication of workflow definitions and workflow results directly on the Galaxy installation.