According to the Industry 4.0 vision, big data management is among the new challenges 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 for big data exploration in a dynamic context of interconnected systems, such as the Industry 4.0 domain. The approach relies on three main pillars: (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 to focus the attention on relevant data only.
Enabling big data exploration in dynamic contexts
Bagozi, Ada;Bianchini, Devis;De Antonellis, Valeria;Ragazzi, Davide
2018-01-01
Abstract
According to the Industry 4.0 vision, big data management is among the new challenges 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 for big data exploration in a dynamic context of interconnected systems, such as the Industry 4.0 domain. The approach relies on three main pillars: (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 to focus the attention on relevant data only.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
