Tourist Behaviour Analysis Based on Digital Pattern of Life

Tourism industry has been actively developing during recent years. Tourists actively produce user-generated content such as photos and videos, use various mobile devices to support, and share their attractions review trips in social networks. This content is a basis for tourist behaviour models construction which allow to predict future desires and intentions. This work presents a tourist behaviour analysis system based on digital pattern of life concept. This concept represents tourist in IT environment and connects them with behaviour analysis instruments. The solution is based on ontological approach, which allows to use context information for the analysis and prediction. The usage of digital pattern of life allows to extract tourists behaviour components in a convenient form for analysis. The paper introduces three behaviour analysis use cases, which can be implemented by using classification, clustering, and time-series prediction machine learning techniques.