Wissenschaftliche Publikationen

Betz, D.; Biniossek, C.; Blanchi, C.; Henninger, F.; Lauer, L.; Wieder, P.; Wittenburg, P.; Zünkeler, M.: Canonical Workflow for Experimental Research. Data Intelligence (4 (2)) (2022)

Decker, J.; Kasprzak, P.; Kunkel, J.: Performance Evaluation of Open-Source Serverless Platforms for Kubernetes. Algorithms 15 (7), 234 (2022)

Doan, T. H. A.; Bingert, S.; Yahyapour, R.: Using a Workflow Management Platform in Textual Data Management. Data Intelligence 4 (2), S. 398 - 408 (2022)

Király, P.; Lowagie, H.: Implementation of a daily MARC assessment with open source tools at KBR, the royal library of Belgium. IFLA Metadata Newsletter Vol. 8 (No. 1, June 2022), S. 12 - 15 (2022)

Köhler, C.; Biniaz, M. H.; Bingert, S.; Nolte, H.; Kunkel, J.: Secure Authorization for RESTful HPC Access with FaaS Support. International Journal on Advances in Security Vol. 15 (No. 3 and 4), S. 119 - 131 (2022)

Nolte, H.; Spicher, N.; Russel, A.; Ehlers, T.; Krey, S.; Krefting, D.; Kunkel, J.: Secure HPC: A Workflow Providing a Secure Partition on an HPC System. Future Generation Computer Systems 141, S. 677 - 691 (2022)

Nolte, H.; Wieder, P.: Realising data-centric scientific workflows with provenance-capturing on data lakes. Data Intelligence 4 (2), S. 426 - 438 (2022)

Ungváry, R.; Király, P.: A MARC21 tételfejének és kódolt tartalmi jellemzőinek feldolgozási minősége néhány nemzeti könyvtárban. Tudományos és Műszaki Tájékoztatás, S. 155 - 175 (2022)

Wieder, P.; Nolte, H.: Toward data lakes as central building blocks for data management and analysis. Front. Big Data Vol. 5, S. 1 - 18 (2022)

Zaznov, I.; Kunkel, J.; Dufour, A.; Badii, A.: Predicting Stock Price Changes Based on the Limit Order Book: A Survey. Mathematics 10 (8) (Series 1234) (2022)

Pencil Code Collaboration; Bingert, S.: The Pencil Code, a modular MPI code for partial differential equations and particles: multipurpose and multiuser-maintained. Journal of Open Source Software (6 [58], 2807) (2021)

Betke, E.; Kunkel, J.: Classifying Temporal Characteristics of Job I/O Using Machine Learning Techniques. Journal of High-Performance Storage (1) (2021)

Carns, P.; Kunkel, J.; Mohror, K.; Schulz, M.: Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332). Dagstuhl Reports, S. 16 - 75 (2021)

Gadban , F.; Kunkel, J.: Analyzing the Performance of the S3 Object Storage API for HPC Workloads. Applied Sciences (11) (2021)

Hauswaldt, J.; Bahls, T.; Blumentritt, A.; Demmer, I.; Drepper, J.; Groh, R.: Sekundäre Nutzung von hausärztlichen Routinedaten ist machbar – Bericht vom RADAR Projekt. Gesundheitswesen (83 (S 02)), S. 130 - 138 (2021)

Király, P.: Kulturális adatelemzés – bevezetés és önéletrajz. Lev Manovich. Cultural analytics. (Cambridge, Massachussetts–London, England: The MIT Press, 2020) – Book Review. Digitális Bölcsészet (4), S. 11 - 22 (2021)

Kunkel, J.; Betke, E.: A Workflow for Identifying Jobs with Similar I/O Behavior Utilizing Time Series. The Journal of High-Performance Storage (2) (2021)

Nachtwey, B.: So oder so – Methoden zur Reorganisation einer DB2-Datenbank. iX – Magazin für professionelle IT (8), S. 132 - 135 (2021)

Sadegh, S.; Zamanifar, K.; Kasprzak, P.; Yahyapour, R.: A two-phase virtual machine placement policy for data-intensive applications in cloud. Journal of Network and Computer Applications Vol. 180 (April) (2021)

Triebel, D.; Ivanovic, D.; Kahila Bar-Gal, G.; Bingert, S.; Weibulat, T.: Towards a COST MOBILISE Guideline for Long Term Preservation and Archiving of Data Constructs from Scientific Collections Facilities. Biodiversity Information Science and Standards (5) (2021)