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In this thesis first we propose an intermediate data management scheme for aSWfMS. In our second attempt, we explored the possibilities and introduced anautomatic recommendation technique for a SWfMS from real-world workflow data(i.e Galaxy [1] workflows) where our investigations show that the proposedtechnique can facilitate 51% of workflow building in a SWfMS by reusingintermediate data of previous workflows and can reduce 74% execution time ofworkflow buildings in a SWfMS. Later we propose an adaptive version of ourtechnique by considering the states of tools in a SWfMS, which shows around 40%reusability for workflows. Consequently, in our fourth study, We have doneseveral experiments for analyzing the performance and exploring theeffectiveness of the technique in a SWfMS for various environments. Thetechnique is introduced to emphasize on storing cost reduction, increase datareusability, and faster workflow execution, to the best of our knowledge, whichis the first of its kind. Detail architecture and evaluation of the techniqueare presented in this thesis. We believe our findings and developed system willcontribute significantly to the research domain of SWfMSs.

An Intermediate Data-driven Methodology for Scientific Workflow Management System to Support Reusability