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Auflistung nach Autor:in "Klassen, Gerhard"

1 - 4 von 4
  • Konferenzbeitrag
    Detection and Implicit Classification of Outliers via Different Feature Sets in Polygonal Chains
    (Datenbanksysteme für Business, Technologie und Web (BTW 2017), 2017) Singhof, Michael; Klassen, Gerhard; Braun, Daniel; Conrad, Stefan
    Many outlier detection tasks involve a classification of outliers of di erent types. Most standard procedures solve this problem in two steps: First, an outlier detection algorithm is carried out, which is normally trained on outlier free data, only, since the samples of outliers are limited. Second, the outliers detected in that step, are classified with a conventional classification algorithm, that needs samples for all classes. However, often the quality of the classification is lowered due to the small number of available samples. Therefore, in this work, we introduce an outlier detection and classification algorithm, that does not depend on training data for the classification process. Instead, we assume, that di erent kinds of outliers are inferred by di erent processes and as such should be detected by different outlier detection approaches. This work focuses on the example of outliers in mountain silhouettes.
  • Workshopbeitrag
    Illuminating the Predictive Power of Gamification to Inspire Technology Users
    (Mensch und Computer 2023 - Workshopband, 2023) Weber, Sebastian; Klassen, Gerhard; Wyszynski, Marc; Kordyaka, Bastian
    This study explores the relationship between gamification design features and the motivational state of inspiration in the context of eLearning. We focus on three dimensions of gamification: immersion, achievement, and social. Using a cross-sectional survey design, covariance-based statistics, and structural equation modeling, we collected data from users of a language learning app. Our findings reveal that achievement-related gamification features, such as badges, points, levels, and tasks, evoke inspiration and foster the inspiration to learn. However, neither immersion-related nor social-related gamification features serve as a source of inspiration. This research contributes to the understanding of how gamification can be leveraged to enhance inspiration and possibly learning outcomes in eLearning environments.
  • Konferenzbeitrag
    Overcoming Inefficiency in Public Procurement: An OpenData Approach
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Klassen, Gerhard; Palombo, Raphael; Bauer, Luca T.; Niehaves, Björn
    This paper discusses the need for an OpenData platform with data-based services to ad- dress the challenges facing the public procurement market. In the last 15 years, public procurement has doubled and now accounts for 15% of GDP. However, there is a shortage of skilled workers, and many tenders are still created manually. By leveraging advanced technologies such as machine learning and predictive analytics, we aim to improve the efficiency and effectiveness of public pro- curement. Our paper highlights the urgent need for a data-driven approach to public procurement and presents our plans for an OpenData platform that can deliver significant benefits to both the public sector and private enterprises.
  • Research Paper
    Turning Tenders into Tinder: How AI and Open Data can spark Bidding Matches
    (7. Fachtagung Rechts- und Verwaltungsinformatik (RVI 2024): Neue Wege der Zusammenarbeit und Vernetzung für digitale Transformation und Verwaltungsmodernisierung, 2024) Klassen, Gerhard; Bauer, Luca T.; Fritzsche, Robin; Kordyaka, Bastian; Weber,Sebastian; Niehaves, Björn
    Public procurement in Germany, accounting for 15% of GDP, is plagued by inefficiencies, high costs, and lack of transparency. This study investigates how Open Data can enhance competitive bidding and streamline the identification of suitable companies. Using the German public procurement market, we propose a web-based portal employing machine learning to automate tender and bidder matchmaking. Our methodology includes data collection, company profiling, and NLPbased similarity searches. Results indicate that integrating Open Data can increase competition, improve bid quality, and enhance procurement efficiency. This research provides a scalable framework for more transparent and effective public procurement practices, with potential applications in other regions and sectors.