A machine learning approach for IoT cultural data

The data science discipline can play a crucial role in developing effective data driven strategies for the valorization and promotion of the cultural heritage (CH) domain. Machine learning approaches can provide new perspectives, allowing knowledge extraction and insights generation from data since in the last decade CH domain has benefited from the applications of internet of things (IoT) solutions in order to improve visitors’ experience. Analyzing a great amount of data increasingly requires the use of advanced mathematical algorithms and therefore requires distribution, calculation and digital protection services. Data represent a great challenge for the CH domain, as well as a resource; this paper presents and discusses the application of a machine learning approach on IoT cultural data collected in the National Archaeological Museum of Naples. With the deployment of some Bluetooth sensing boards we collected the visit paths of the users in a non-invasive way. The research goal is to analyze and classify the collected visiting behavioural data in order to produce useful insights for cultural stakeholders. The knowledge of people behaviours can help museum organizations both in terms of medium-long term strategy and also in terms of strictly operational decisions.