Automated Creation and Provisioning of Decision Information Packages for the Smart Factory

In recent years, Industry 4.0 emerges as a new trend, enabling the integration of data-intensive cyber physical systems, Internet of Things, and mobile applications, into production environments. Even though Industry 4.0 concentrates on automated engineering and manufacturing processing, the human actor is still important for decision making in the product lifecycle process. To support correct and efficient decision making, human actors have to be provided with relevant data depending on the current context. This data needs to be retrieved from distributed sources like bill of material systems, product data management and manufacturing execution systems, holding product model and factory model. In this article, we address this issue by introducing the concept of decision information packages, which enable to compose relevant engineering data for a specific context from distributed data sources. To determine relevant data, we specify a context-aware engineering data model and corresponding operators. To realize our approach, we provide an architecture and a prototypical implementation based on requirements of a real case scenario. This article is a revised and selected version of the previous work

Implementing a Query Language for Context-dependent Semistructured Data

Abstract. In today’s global environment, the structure and presentation of information may depend on the underlying context of the user. To address this issue, in previous work we have proposed multidimensional semistructured data (MSSD), where an information entity can have alternative variants, or facets, each holding under some world, and MOEM, a data model suitable for representing MSSD. In this paper we briefly present MQL, a query language for MSSD that supports context-driven queries, and we attempt to motivate the direct use of context in data models and query languages by comparing MOEM and MQL with equivalent, context-unaware forms of representing and querying information. Specifically, we implemented an evaluation process for MQL during which MQL queries are translated to equivalent Lorel queries, and MOEM databases are transformed to corresponding OEM databases. The comparison between the two query languages and data models demonstrates the benefits of treating context as first-class citizen. We illustrate this query translation process using a cross-world MQL query, which has no direct counterpart in contextunaware query languages and data models.