Machine Learning and Deep Learning in Project Analytics: Methods, Applications and Research Trends

Abstract

Project analytics refers to applying analytical techniques and methods to past and present data to gain insights into how the underlying project is performing. Machine learning and deep learning have gained extensive usage in various disciplines due to their analytical strength and the availability of high-speed computational devices. This paper presents a comprehensive survey of commonly used machine learning and deep learning algorithms in the project analytics domain for addressing project-related research problems. We have several notable observations from this survey. First, Support vector machine and Random forest are the most commonly used machine learning algorithms. It is the Artificial neural network among the deep learning algorithms. Second, there has been a sharp increase in the application of machine learning and deep learning algorithms in the project analytics context in recent years. Third, these algorithms are mainly applied to construction and software project contexts for researching attributes related to cost and time performance. Overall, the project analytics domain continues to grow with machine learning and deep learning applications in addressing project-related research problems in the coming years.

Keywords: Project Analytics, machine learning, Deep learning, Research trends, research methods, and Research applications