IBC and Zenith Seminar: Daniel de Oliveira “Parameter and Data Recommendation in Scientific Workflows based on Provenance”, 5 June 2018

Abstract: A growing number of data- and compute-intensive experiments have been modeled as scientific workflows in the last years. Such experiments are commonly executed several types varying parameters and input data files since the comparing method plays an important role in scientific research. As the complexity of the experiments and the volume of input and intermediate data increase, scientists have to spend much time defining parameter values and data files to be processed in such experiments. This talk discusses the problem of identifying suitable parameter values and data files for an experiment and then recommending them for the scientist. We present a novel method to make recommendations for scientists. This method is based on data captured from previous executions of the workflow and machine learning algorithms. Our experiments show that, the recommended data files and parameters do a good job in helping scientists to execute workflow successfully.