Songhai Lin, Hong Xiao, Wenjun Jiang, Dafeng Li, Jiaben Liang, Zelin Li
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The automation of data analysis in the form of scientific workflows has become a widely adopted practice in many fields of research. Data-intensive experiments using workflows enabled automation and provenance support, which contribute to alleviating the reproducibility crisis. This paper investigates the existing provenance models as well as scientific workflow applications. Furthermore, here we not only summarize the models at different levels, but also compare the applications, particularly the blockchain applied to the provenance in scientific workflows. After that, a new design of secure provenance system is proposed. Provenance that would be enabled by the emerging technology is also discussed at the end.
期刊介绍：
The Journal of High Speed Networks is an international archival journal, active since 1992, providing a publication vehicle for covering a large number of topics of interest in the high performance networking and communication area. Its audience includes researchers, managers as well as network designers and operators. The main goal will be to provide timely dissemination of information and scientific knowledge.
The journal will publish contributed papers on novel research, survey and position papers on topics of current interest, technical notes, and short communications to report progress on long-term projects. Submissions to the Journal will be refereed consistently with the review process of leading technical journals, based on originality, significance, quality, and clarity.
The journal will publish papers on a number of topics ranging from design to practical experiences with operational high performance/speed networks.
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