data integration

Software development is fundamental to digital transformation. A good foundation of data is required for developers to tailor software to the needs of the commissioning department. Unfortunately, the data models required for this are incomplete, often created unilaterally by the development department and not embedded in the business context. This makes it difficult for both developers and AI to find the right algorithms. The present approach increases understanding and exchange between the specialist and development departments and offers digital assistance with data modeling as a basis for software development. Furthermore, AI approaches can help to increase the quality and completeness of the data.

Supported by an intelligent assistance system, the sustainability and digitalisation of the tideindependent Industriehafen Bremen can be increased. To guarantee port security, a constant level of the isolated harbour is essential. There is great potential for increasing energy efficiency if the lock naturally waters the harbour basin at times of high tide instead of employing a pump station. This contribution shows how artificial intelligence and innovative software systems were used to develop an assistance system to improve existing procedures without making extensive changes to the existing port infrastructure. (Only in German)

The data generated in the design and manufacture of tools and dies is rarely used in a targeted manner. However, it contains a significant wealth of knowledge that can be used to predict important parameters. For example, the metadata generated during the CAM programming of die-sinking electrodes can be used to extract parameters suitable to predict the subsequent erosion duration. A major hurdle in making data usable is their heterogeneity in number, format and employed terminology. This article presents a structured method for integrating heterogeneous data and making them queryable via a uniform interface. Its added value is demonstrated by means of a use case from die-sinking EDM.

Sophisticated port infrastructure systems often have a significant potential for increasing energy efficiency and optimising internal processes. Supported by intelligent and innovative methods, solutions are to be created to improve existing procedures without having to make large-scale changes to the port infrastructure. The specific application scenario of intelligent processes is a tidal water port in Northern Germany.

In terms of Industrie 4.0, especially SMEs are facing the challenge of integrating data both vertically and horizontally. To achieve this task, common solutions such as ERP are increasingly replaced by manufacturing executions systems (MES). Due to the direct connection in production, MES allow a production control and serve as bridge between planning and manufacturing level. Data integration is furthermore the basis for an automated risk management in production. The research project quadrika develops an MES module that predictively recognizes risks and thus prevents faults.

The digital transformation led to disruptive changes in business models of leading companies. Big Data serves as one of the key enables in this area. The transfer of this concept in the production domain towards an Industrial Big Data is key challenge for producing companies. Although exemplary key projects exist, no available characterization of structural elements in Industrial Big Data Processes exists. Therefore, this article aims at presenting initial structural elements of Industrial Big Data projects based on exemplary use cases.