TY - CHAP A1 - Gottwald, Susanne A1 - Tolksdorf, Robert T1 - Planning Teams with Semantic Web Technologies T2 - INFORMATIK 2009 - Im Focus das Leben N2 - When planning teams for projects with specific goals, employees of a company have to group together so well, that all necessary knowledge for conquering the project’s challenges are met within the member’s skills. A tool that facilitates semantic web technologies can support the team recruiter, who is responsible for chosing the members of the team, in terms of finding the most efficient combinations of the company’s employees based on their expertises. Y1 - 2009 SN - ISBN 978-3-88579-248-2 SN - ISSN 1617-5468 ER - TY - CHAP A1 - Klindt, Marco T1 - PDF/A considered harmful for digital preservation T2 - Proceedings of iPres – 14th International Conference on Digital Preservation / Kyoto, Japan / September 25 – 29, 2017 N2 - Today, the Portable Document Format (PDF) is the prevalent file format for the exchange of fixed content electronic documents for publication, research, and dissemination work in the academic and cultural heritage domains. Therefore it is not surprising that PDF/A is perceived to be an archival format suitable for digital archiving workflows. This paper gives a rather short overview about the history and technical complexity of the format, its benefits, shortcomings and potential pitfalls in the area of digital preservation with respect to aspects of accessibility and reusability of the information content of PDF/A. Several potential problems within the creation, preservation, and dissemination contexts are identified that may create problems for present and future content users. It also discusses some of the risks inherent to PDF/A for parts of the preservation community and suggests possible strategies to mitigate problems that might prevent future human or machine-based usability of the data and information stored within digital archives. Y1 - 2017 UR - https://phaidra.univie.ac.at/o:931063 VL - 14 ER - TY - CHAP A1 - Yueksel Erguen, Inci A1 - Litzel, Ida A1 - Peng, Hanqiu T1 - Integrating Large Citation Datasets T2 - Operations Research Proceedings 2024. OR 2024 N2 - This paper explores methods for building a comprehensive citation graph using big data techniques to evaluate scientific impact more accurately. Traditional citation metrics have limitations, and this work investigates merging large citation datasets to create a more accurate picture. Challenges of big data, like inconsistent data formats and lack of unique identifiers, are addressed through deduplication efforts, resulting in a streamlined and reliable merged dataset with over 119 million records and 1.4 billion citations. We demonstrate that merging large citation datasets builds a more accurate citation graph facilitating a more robust evaluation of scientific impact. Y1 - 2025 U6 - https://doi.org/10.1007/978-3-031-92575-7_7 SP - 46 EP - 52 ER -