Prater, Ryan

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Prater
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Ryan
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Ryan Prater

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  • Vorschaubild
    Publikation
    Identification and chaining of water accounting data stakeholders
    (EasyChair, 2022) Prater, Ryan; Eisenbart, Barbara; Hinkelmann, Knut; Gerber, Aurona [in: Proceedings of the society 5.0 conference 2022. Integrating digital world and real world to resolve challenges in business and society]
    Purpose – Multiple water accounting techniques exist and suffer from data gaps and misaligned stakeholders which creates standardization and consolidation problems in the data of the industry. This study identifies domain-based stakeholders and defines stakeholder data relationships to improve inter-stakeholder data efficiency. Design/methodology/approach – The research design follows an inductive data collection of qualitative cross-sectional data through semi-structured expert interviews. The recorded interviews were transcribed, thematically coded, and the findings summarized. Findings – The result is an improved specificity of water accounting data stakeholders which have different data input and output requirements. Our research found that these stakeholders can be chained together based on their data relationships which enables identifying inter-stakeholder relationships and improved data efficiency. Social Implications – Water is a vital resource for humans and the United Nations Sustainable Development Goals. More precise description of stakeholders and data factors enable more efficient data flow which can improve the efficacy of terminal impact. Originality/value – The awareness of problem is refined by increasing stakeholder specificity and identifying data input/output requirements. This enables chaining of stakeholders and data to clarify stakeholder data requirements and improve data efficiency for purposes such as collaboration and policy guidance.
    04B - Beitrag Konferenzschrift
  • Vorschaubild
    Publikation
    A hybrid intelligent approach for the support of higher education students in literature discovery
    (2022) Prater, Ryan; Laurenzi, Emanuele; Martin, Andreas; Hinkelmann, Knut; Fill, Hans-Georg; Gerber, Aurona; Lenat, Doug; Stolle, Reinhard; van Harmelen, Frank [in: Proceedings of the AAAI 2022 Spring Symposium on Machine Learning and Knowledge Engineering for Hybrid Intelligence (AAAI-MAKE 2022)]
    In this paper, we present a hybrid intelligent approach that combines knowledge engineering, machine learning, and human intervention to automatically recommend literature resources relevant for a high quality of literature discovery. The primary target group that we aim to support is higher education students in their first experiences with research works. The approach builds a knowledge graph by leveraging a logistic regression algorithm which is first parameterized and then influenced by the interventions of a supervisor and a student, respectively. Both interventions allow continuous learning based on both the supervisor’s preferences (e.g. high score of H-index) and the student’s feedback to the resulting literature resources. The creation of the hybrid intelligent approach followed the Design-Science Research methodology and is instantiated in a working prototype named PaperZen. The evaluation was conducted in two complementary ways: (1) by showing how the design requirements manifest in the prototype, and (2) with an illustrative scenario in which a corpus of a research project was taken as a source of truth. A small subset from the corpus was entered into the PaperZen and Google Scholar, independently. The resulting literature resources were compared with the corpus of a research project and show that PaperZen outperforms Google Scholar
    04B - Beitrag Konferenzschrift