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In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case.

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The Digital Product Passport (DPP) is a concept for collecting and sharing product-related information along the life cycle of a product. DPPs are currently the subject of intense discussion, and various development efforts are being undertaken. These are supported by regulatory activities, especially in the case of the battery passport. The aggregation of product life-cycle data and their respective use, as well as the sharing of these data between companies, entrepreneurs, and other actors in the value chain, is crucial for the creation of a resource-efficient circular economy. Despite the urgent need for such a solution, there is currently little attention given to the digital infrastructure for the creation and handling of the DPPs (i.e., the so-called DPP system). Moreover, there is so far no common understanding of what the requirements for a DPP system are. This is the background and underlying motivation of our paper: we identify the requirements for a DPP system in a structured way, i.e., based on stakeholder involvement and current literature from science and industry. In addition, we compose, categorize, and critically analyze the results, i.e., the list of requirements for DPP systems, in order to identify gaps. Summarized, our research provides insights into the criteria to be considered in the creation of an actual DPP system.

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