Using Data Mining in Combination with Machine Learning to Enhance Crowdsourcing of a Formal Model of Biodiesel Production

摘要

Formal modeling, simulation, and analysis of complex systems is valuable because it can provide insights into complex systems that are too expensive or difficult to analyze otherwise. In this work, we present an approach for improving simulation trajectory choices in a Monte Carlo framework using a combination of crowdsourcing, machine learning, and data mining. We apply machine learning to analysis of a formal model of biodiesel production as a method of improving the efficiency of the crowd sourced mobile simulation analysis of the model. Data is collected and data mined in a central server where machine learning is applied and recommendations from the machine learning algorithm are fed back to crowd workers via suggestions on the mobile app. Ultimately, we show that this approach can improve efficiency of optimal safe state identification in the biodiesel model analysis.