Logo des Repositoriums

Auflistung nach Autor:in "Latif, Atif"

1 - 3 von 3
  • Textdokument
    Challenges in Creating a Sustainable Generic Research Data Infrastructure
    (Softwaretechnik-Trends: Vol. 37, No. 2, 2017) Grunzke, Richard; Müller-Pfefferkorn, Ralph; Nagel, Wolfgang E.; Adolph, Tobias; Biardzki, Christoph; Frank, Anton; Bode, Arndt; Kazakova, Anastasia; Limani, Fidan; Latif, Atif; Busch, Anja; Borst, Timo; Tochtermann, Klaus; Neumann, Mathis; Sousa, Nelson Tavares de; Thomsen, Ingo; Hasselbring, Wilhelm; Tendel, Jakob; Bungartz, Hans-Joachim; Grimm, Christian
    Research data management is of the utmost importance in a world where research data is created with an ever increasing amount and rate and with a high variety across all scientific disciplines. This paper especially discusses software engineering challenges stemming from creating a long-living software system. It aims at providing a reference implementation for a federated research data infrastructure including interconnected individual repositories for communities and an overarching search based on metadata. The challenges involve a high variety of evolving requirements, the management and development of the distributed and federated infrastructure that are based on exist- ing components, the piloting within the use cases, the efficient training of users, and how to enable the future sustainable operation.
  • Zeitschriftenartikel
    LOD for Library Science: Benefits of Applying Linked Open Data in the Digital Library Setting
    (KI - Künstliche Intelligenz: Vol. 30, No. 2, 2016) Latif, Atif; Scherp, Ansgar; Tochtermann, Klaus
    Linked Open Data (LOD) has gained widespread adoption by large industries as well as non-profit organizations and governmental organizations. One of the early adopters of LOD technologies are libraries. Since the “early years”, libraries have been key use case and innovation driver for LOD and significantly contributed to the adoption of semantic technologies. The first part of this paper presents selected success stories of current activities in the Linked Data Library community. In a nutshell, these studies include (1) a conceptualization of the Linked Data Value chain, (2) a case study for consumption of Linked Data in a digital journal environment, and (3) an approach to publish metadata on the Semantic Web from an Open Access repository. These stories reveal a strong relationship between LOD in libraries and research topics addressed in traditional fields of computer science such as artificial intelligence, databases, and knowledge discovery. Thus, in the second part of this paper we systematically review the relation of LOD in digital libraries from a computer science perspective. We discuss current LOD research topics such as data integration and schema integration, distributed data management, and others. These challenges have been discussed with computer scientists at a German national database meetup as well as with librarians from ZBW—Leibniz Information Center for Economics and at international librarians meetup.
  • Konferenzbeitrag
    NFDI4DS Infrastructure and Services
    (INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, 2023) Schimmler, Sonja; Wentzel, Bianca; Bleier, Arnim; Dietze, Stefan; Karmakar, Saurav; Mutschke, Peter; Kraft, Angelie; Taffa, Tilahun A.; Usbeck, Ricardo; Boukhers, Zeyd; Auer, Sören; Castro, Leyla J.; Ackermann, Marcel R.; Neumuth, Thomas; Schneider, Daniel; Abedjan, Ziawasch; Latif, Atif; Limani, Fidan; Abu Ahmad, Raia; Rehm, Georg; Attar Khorasani, Sima; Lieber, Matthias
    NFDI4DataScience (NFDI4DS) is a consortium founded to support researchers in all stages of the research data lifecycle in order to conduct their research in line with the FAIR principles. The infrastructure developed targets researchers from a wide range of disciplines working in the field of data science and artificial intelligence. NFDI4DS contributes to systematically understanding the needs and challenges of researchers in various disciplines regarding data science and artificial intelligence, keeping in mind ethical, legal and social aspects. The identified needs will be addressed by support structures such as educational videos and challenges. Transparency, reproducibility and FAIRness will be improved by integrating existing and newly developed services into the NFDI4DS infrastructure, and by systematically adding all digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) to the NFDI4DS research knowledge graph. This paper presents the goals of NFDI4DS, and gives an overview on what the consortium is going to contribute to the data science and artificial intelligence communities. It focuses on existing and newly developed services and their integration.