Related papers: Scientific Workflow Repeatability through Cloud-Aw…
Provenance has been thought of a mechanism to verify a workflow and to provide workflow reproducibility. This provenance of scientific workflows has been effectively carried out in Grid based scientific workflow systems. However, recent…
The emergence of Cloud computing provides a new computing paradigm for scientific workflow execution. It provides dynamic, on-demand and scalable resources that enable the processing of complex workflow-based experiments. With the ever…
Scientific workflows are becoming increasingly popular for compute-intensive and data-intensive scientific applications. The vision and promise of scientific workflows includes rapid, easy workflow design, reuse, scalable execution, and…
One of the foundations of science is that researchers must publish the methodology used to achieve their results so that others can attempt to reproduce them. This has the added benefit of allowing methods to be adopted and adapted for…
In the world of science new technology have opened up the possibility to rely on advanced computational methods and models to conduct and produce scientific research. An important aspect of scientific and business workflows is provenance -…
Scientific workflow has become essential in software engineering because it provides a structured approach to designing, executing, and analyzing scientific experiments. Software developers and researchers have developed hundreds of…
In complex data analyses it is increasingly important to capture information about the usage of data sets in addition to their preservation over time to ensure reproducibility of results, to verify the work of others and to ensure…
Provenance plays a crucial role in scientific workflow execution, for instance by providing data for failure analysis, real-time monitoring, or statistics on resource utilization for right-sizing allocations. The workflows themselves,…
Recording the provenance of scientific computation results is key to the support of traceability, reproducibility and quality assessment of data products. Several data models have been explored to address this need, providing…
Progress in science is deeply bound to the effective use of high-performance computing infrastructures and to the efficient extraction of knowledge from vast amounts of data. Such data comes from different sources that follow a cycle…
The increasing availability of cloud computing services for science has changed the way scientific code can be developed, deployed, and run. Many modern scientific workflows are capable of running on cloud computing resources. Consequently,…
Provenance information are essential for the traceability of scientific studies or experiments and thus crucial for ensuring the credibility and reproducibility of research findings. This paper discusses a comprehensive provenance framework…
Many existing scientific workflows require High Performance Computing environments to produce results in a timely manner. These workflows have several software library components and use different environments, making the deployment and…
Scientific workflows are powerful tools for management of scalable experiments, often composed of complex tasks running on distributed resources. Existing cyberinfrastructure provides components that can be utilized within repeatable…
Science is conducted collaboratively, often requiring the sharing of knowledge about computational experiments. When experiments include only datasets, they can be shared using Uniform Resource Identifiers (URIs) or Digital Object…
Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In…
Scientific workflows are critical to scientific data analysis and often involve computationally intensive processing of large datasets on compute clusters. As such, their execution tends to be long-running and resource-intensive, resulting…
Over the last years, scientific workflows have become mature enough to be used in a production style. However, despite the increasing maturity, there is still a shortage of tools for searching, adapting, and reusing workflows that hinders a…
To benefit from the abundance of data and the insights it brings data processing pipelines are being used in many areas of research and development in both industry and academia. One approach to automating data processing pipelines is the…
Conducting experiments and documenting results is daily business of scientists. Good and traceable documentation enables other scientists to confirm procedures and results for increased credibility. Documentation and scientific conduct are…