Multi-source, unstructured and external data analytics for manufacturing process

Multi-source, unstructured and external data analytics for manufacturing process Taehoon Ko Department of Industrial Engineering The Graduate School Seoul National University Data integration means the task of combining data with various types residing at different sources, and providing the user with a unified view of these data. In this thesis, we consider the data integration as the process of creating data marts to be used as input to the machine learning and data mining models in a view of data analyzer and miners. Actually, three types of problems are encountered in the data integration process: How to integrate (1) data from various sources, (2) different types of data and (3) external data with internal data. To integrate these data, the enterprise must consider and solve some technical and manageral issues. To prove our concept, three real-world applications are introduced. Knowledge can be regarded as the most valuable asset of a manufacturing enterprise. Therefore, a manufacturer enterprise should collect the data representing its processes and environments and analyze the data to build a sustainable knowledge model. First application is about generating user scenarions using online social media in the early steps of new product development (NPD) process. By strategic keyword searching, several novel user contexts are discovered from online social media. Based on contexts, domain experts can generate user scenarios for new features and functions of the target product. Second application is to construct early engine fault detection models by integrating manufacturing, inspection and after-sales service data. In most cases, production data and after-sales service data are managed independent departments,