Summarisation and relevance evaluation techniques for big data exploration: The smart factory case study

The increasing connections of systems that produce high volumes of real time data have raised the importance of addressing data abundance research challenges. In the Industry 4.0 application domain, for example, high volumes and velocity of data collected from machines, as well as value of data that declines very quickly, put Big Data issues among the new challenges also for the factory of the future. While many approaches have been developed to investigate data analysis, data visualisation, data collection and management, the impact of Big Data exploration is still under-estimated. In this paper, we propose an approach to support and ease exploration of real time data in a dynamic context of interconnected systems, such as the Industry 4.0 domain, where large amounts of data must be incrementally collected, organized and analysed on-the-fly. The approach relies on: (i) a multi-dimensional model, that is suited for supporting the iterative and multi-step exploration of Big Data; (ii) novel data summarisation techniques, based on clustering; (iii) a model of relevance, aimed at focusing the attention of the user only on relevant data that are being explored. We describe the application of the approach in the smart factory as a case study.