Industrie 4.0 environments generate an unprecedented amount of production data. This is due to the rising number of sensors and interconnected devices capable of emitting data in millisecond frequencies. Streaming analytics offers promising methodologies that can support handling and analysis of data volume and variety. Transparency and control over real-time data can increase production efficiency in tightly connected machine environments. Data transparency may avoid time-consuming assessment of machines to detect anomalous machine behavior causing production inefficiencies or failures. This paper aims to identify requirements to implement streaming analytics for the detection of anomalies in Industrie 4.0 production machine groups through a structured literature review.

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Blended learning courses offer the opportunity to collect large amounts of learning data that can help students to improve their performance. The presentation of learning data often takes place in the form of Learning Analytics dashboards, which are already in use at some universities. Students, who are the primary data providers and at the same time the main users, should be involved in the process of developing Learning Analytics dashboards from the beginning. Since there are only a few guidelines for designing these dashboards in literature, we conducted a study with 139 business and information systems students who, in addition to answering a questionnaire, also designed their dashboards with the help of a case study. The dashboard analysis provides detailed insights into the design of the functional and information scope, as well as the presentation of the data for Learning Analytics dashboards.