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

Context Data Management for Large Scale Context-Aware Ubiquitous Systems

Ph.DDOCTOR OF PHILOSOPH

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen