The Spatio-Temporal Modeling and Integration of Manufacturing Big Data in Job Shop: An Ontology-Based Approach

Manufacturing big data provide the factory with a tremendous opportunity for transforming the current manufacturing paradigm to smart manufacturing. However, the multi-source data modeling and integration problems are the existing gaps between the collected big data and the data-driven smart applications. With the large-scale deployment of Internet of things on the shop floor, it is essential to develop adequate data modeling and integration methods to manage and organize the generated manufacturing big data. In this study, the spatiotemporal modeling is firstly presented to organize the data in temporal, spatial and attributive dimensions respectively. Furthermore, the ontology-based big data integration approach is proposed to manage the multisource manufacturing data and ensure the data can be easily indexed and conveniently reused for different subsequent applications. Finally, the proposed data modeling and integration methods are implemented and verified through the developed manufacturing big data-driven analysis and decision-making system.