The objective of this work is to improve the initial solution search and selection process for data science projects, enable interoperability and reuse of existing solutions from different disciplines in a single integrated workflow, and advance traceability between design and implementation of data science projects. I propose a method that enables documenting software solutions in a central repository which provides efficient and effective prototyping of solutions for problems that have similar characteristics to previously developed solutions and allows execution of modules of different software languages in a single workflow.

A knowledge management framework that utilizes systems engineering tools, process modeling, and ontologies is proposed for documenting process-oriented data science solutions and generating potential solutions for new problem instances. Using this framework, I document solutions from the presented case study in a systematic way by using formal process modeling to document the solution workflows, generalize the solution methods by creating a process template and categorizing each part of the solution as a part of the template, and define traceability links from the problem requirements to each solution method so that new solutions of the generalized workflow can be instantiated given a new problem and be executed in simulation tools. I also provide evidence that supports the utility of this approach by demonstrating both efficient and effective prototyping of solutions for new problems and show that the proposed method enables executing software modules written in different software languages in a single workflow.