Lecture Notes in Informatics

Permanent URI for this communityhttps://dl.gi.de/handle/20.500.12116/21

Die „GI-Edition: Lecture Notes in Informatics" (LNI) ist eine eigene Veröffentlichungsreihe der GI mit den Strängen: Proceedings, Dissertations, Seminars und Thematics. Alle in den LNI herausgegebenen Bände werden von GI-Gliederungen unterstützt und verantwortet.

Information und Ansprechpartner zur Reihe finden sich auf den Webseiten der GI unter https://gi.de/service/publikationen/lni

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Authors with most Documents  

Schroeder, Ulrik
79
Lucke, Ulrike
67
Busch, Christoph
59
Thomas, Oliver
58
Theuvsen, Ludwig
57

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Author: search.filters.author.Klassen, GerhardSubject: search.filters.subject.Machine Learning

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  • Research Paper
    Turning Tenders into Tinder: How AI and Open Data can spark Bidding Matches
    (Gesellschaft für Informatik e.V., 2024) Klassen, Gerhard; Bauer, Luca T.; Fritzsche, Robin; Kordyaka, Bastian; Weber,Sebastian; Niehaves, Björn; Wimmer, Maria A.; Räckers, Michael; Hünemohr, Holger
    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.
  • Conference Paper
    Overcoming Inefficiency in Public Procurement: An OpenData Approach
    (Gesellschaft für Informatik e.V., 2023) Klassen, Gerhard; Palombo, Raphael; Bauer, Luca T.; Niehaves, Björn; Klein, Maike; Krupka, Daniel; Winter, Cornelia; Wohlgemuth, Volker
    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.