• International journal of advanced smart convergence
• /
• v.10 no.4
• /
• pp.72-83
• /
• 2021

The use of the predictive analytics (PA) powered by the artificial intelligence (AI) is more important in the movie sector during the COVID-19 pandemic, because Hollywood witnessed the impact of the 'Netflix Effect' and began to invest in data and AI. Our purpose is to discover a few cases of the AI centered PA in the movie industry value chain based on five objectives of PA: Compete, grow, enforce, improve, and satisfy. Even if movie companies' interest is to predict future success for competing with over-the-tops (OTTs) at a first glance, it is observed, once they start to use the PA with the AI, they try to utilize the enhanced PA platforms for remaining four objectives. As a result, ScriptBook, Vault, Pilot, Cinelytic and Merlin Video (Merlin) are use cases for the objective 'compete.' Movio of Vista Group International and Datorama of Salesforce are use cases for the objective 'grow.' Industrial Light & Magic (ILM) and Geena Davis Institute on Gender in Media (GDI) with Disney are use cases for the objective 'enforce.' Watson, Benjamin, and Greenlight Essential are use cases for the objective 'improve.' Disney Research (DR) with Simon Fraser University and California Institute of Technology is the use case for the objective 'satisfy.'

• Korean Journal of Construction Engineering and Management
• /
• v.22 no.6
• /
• pp.76-86
• /
• 2021

Construction projects have high potentials of claims and disputes due to inherent risks where a variety of stakeholders are involved. Since disputes could cause losses in terms of cost and time, it is a critical issue for contractors to forecast and pro-actively manage disputes in advance in order to secure project efficiency and higher profits. The objective of the study is to develop a decision tree-based predictive analytics model for forecasting dispute types and their probabilities according to construction project conditions. It can be a useful tool to forecast potential disputes and thus provide opportunities for proactive management.

• Korean Journal of Construction Engineering and Management
• /
• v.22 no.5
• /
• pp.55-65
• /
• 2021

Compared with other industries, construction industry shows a higher rate of death accidents and recently companies' legal responsibilities are to be increasingly enforced. The trend causes tremendous concerns for construction firms and increases the importance of forecasting and pro-actively managing death accidents in construction fields. The objective of the study is to develop a predictive analytics model for forecasting death accidents in building projects based on a decision tree technique, which enables to forecast the probabilities of death accidents by trade. The use of the model helps to decrease risks of legal punishments and to assist the safe execution of building projects by forecasting and pro-actively managing death accidents.