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Auflistung nach Schlagwort "data analytics"

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  • Konferenzbeitrag
    Enhancing educational insights: A real-time data analytics stack for project-basedlearning
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Gücük, Gian-Luca; Simic, Dejan; Leible, Stephan; Lewandowski, Tom; Kučević, Emir
    This paper presents a real-time data analytics (DA) stack designed for a project-based course utilizing Jira for project management at a university. The DA stack follows an Extract, Transform, and Load process to visualize students’ usage data within dashboards. The DA stack supports course management by providing insights into students’ activities and progress. We demonstrate the DA stack’s effectiveness through an evaluative case study, which was found to support course objectives and foster improved behavioral adaptations from lecturers to students. Furthermore, we propose a generic DA stack for generalizing and adopting it for similar applications, considering the extensibility and maintainability inherent in the open-source tools used. Moreover, we provide the GitHub repository to view our source code. This study contributes to the relatively underexplored field of real-time learning analytics and offers a starting point for the customization and adoption of the proposed DA stack in different educational contexts.
  • Zeitschriftenartikel
    Industrial analytics – An overview
    (it - Information Technology: Vol. 64, No. 1-2, 2022) Gröger, Christian
    The digital transformation generates huge amounts of heterogeneous data across the industrial value chain, from simulation data in engineering, over sensor data in manufacturing to telemetry data on product use. Extracting insights from these data constitutes a critical success factor for industrial enterprises, e. g., to optimize processes and enhance product features. This is referred to as industrial analytics, i. e., data analytics for industrial value creation. Industrial analytics is an interdisciplinary subject area between data science and industrial engineering and is at the core of Industry 4.0. Yet, existing literature on industrial analytics is fragmented and specialized. To address this issue, this paper presents a holistic overview of the field of industrial analytics integrating both current research as well as industry experiences on real-world industrial analytics projects. We define key terms, describe typical use cases and discuss characteristics of industrial analytics. Moreover, we present a conceptual framework for industrial analytics that structures essential elements, e. g., data platforms and data roles. Finally, we conclude and highlight future research directions.