A Process to Develop Lean Big-Data Platform Architectures for Industrial Manufacturing Contexts

Manufacturing companies can accomplish increased productivity by using data-driven learning methods based on production and machine data. To realise that big-data platforms are needed to simplify data provisioning and manipulation, for example to train machine learning algorithms. We introduce a method for the generation of big-data platform architectures. The method covers software selection which minimises implementation efforts and maximises the maintainability of used software solutions to implement architectures. We apply requirements from four large scale manufacturers to our method in a case study.