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As scientific workflows and the data they operate on, grow in size and complexity, the task of defining how those workflows should execute (which resources to use, where the resources must be in readiness for processing etc.) becomes proportionally more difficult. While "workflow compilers", such as Pegasus, reduce this burden, a further problem arises: since specifying details of execution is now automatic, a workflow's results are harder to interpret, as they are partly due to specifics ofdoi:10.1109/e-science.2007.22 dblp:conf/eScience/MilesDGVMM07 fatcat:dn7i4mxwoffzlpa65ks4dykis4