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These workflows come from a paper by Bharathi, et al. [1]. There is another paper with more information about the workflows by Juve, et al. [2].
The code used to generate these workflows is available here. The code generator sometimes generates negative task runtimes, so watch out for that.
A large collection of DAXes similar to the ones listed above below is available here. Note that it is about 290MB.
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These workflows come from a report by Ramakrishnan and Gannon [23].
The Python code used to generate the DAX files below, as well as several others, can be downloaded here.
Workflow Type | Figure in Report | Example | DAX |
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LEAD Mesoscale Meteorology | Figure 1 | leadmm.xml | |
LEAD ARPS Data Analysis System | Figure 2 | ||
LEAD Data Mining Workflow | Figure 3 | leaddm.xml | |
Storm Surge SCOOP Workflow | Figure 4 | ||
Floodplain Mapping | Figure 5 | floodplain.xml | |
Glimmer | Figure 6 | glimmer.xml | |
Gene2Life | Figure 7 | gene2life.xml | |
Motif Network | Figure 8 | ||
MEME-MAST | Figure 9 | mememast.xml | |
Molecular Sciences | Figure 10 | molsci.xml | |
Avian Flu | Figure 11 | ||
caDSR | Figure 12 | cadsr.xml | |
Pan-STARRS Load | Figure 13 | ||
Pan-STARRS Merge | Figure 14 | ||
McStas | Figure 15 | mcstas.xml |
The Python code used to generate the above DAX files, as well as several others, can be downloaded here.
[1] S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi, “Characterization of Scientific Workflows”, 3rd Workshop on Workflows in Support of Large Scale Science (WORKS 08), 2008.
[2] , "Characterizing and Profiling Scientific Workflows", Future Generation Computer Systems, 29:3, pp. 682–692, March 2013.
[3] L. Ramakrishnan and D. Gannon, "A Survey of Distributed Workflow Characteristics and Resource Requirements", Indiana University Technical Report TR671, 2008.