What we’ve created with Steep

You want to know what Steep can do for you?

Publications

Steep has appeared in a number of scientific publications.

Krämer, M., Bormann, P., Würz, H. M., Kocon, K., Frechen, T., & Schmid, J.
(2024)

A cloud-based data processing and visualization pipeline for the fibre roll-out in Germany

Journal of Systems and Software, 211, 112008. https://doi.org/10.1016/j.jss.2024.112008
Würz, H. M., Kocon, K., Pedretscher, B., Klien, E., & Eggeling, E.
(2023)

A Scalable AI Training Platform for Remote Sensing Data

Kaster, M., & Würz, H. M.
(2022)

Preprocessing of Terrain Data in the Cloud using a Workflow Management System

Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA, 40–49. https://doi.org/10.5220/0011145000003269
Krämer, M.
(2021)

Efficient Scheduling of Scientific Workflow Actions in the Cloud Based on Required Capabilities

In S. Hammoudi, C. Quix, & J. Bernardino (Eds.), Data Management Technologies and Applications. Communications in Computer and Information Science (Vol. 1446, pp. 32–55). Springer. https://doi.org/10.1007/978-3-030-83014-4_2
Krämer, M., Würz, H. M., & Altenhofen, C.
(2021)

Executing cyclic scientific workflows in the cloud

Journal of Cloud Computing, 10(25), 1–26. https://doi.org/10.1186/s13677-021-00229-7
Krämer, M.
(2020)

Capability-based Scheduling of Scientific Workflows in the Cloud

Proceedings of the 9th International Conference on Data Science, Technology, and Applications DATA, 43–54. https://doi.org/10.5220/0009805400430054
Krämer, M.
(2018)

A Microservice Architecture for the Processing of Large Geospatial Data in the Cloud

Doctoral dissertation. Technische Universität Darmstadt. https://doi.org/10.13140/RG.2.2.30034.66248
Böhm, J., Bredif, M., Gierlinger, T., Krämer, M. …
(2016)

The IQmulus Urban Showcase: Automatic Tree Classification and Identification in Huge Mobile Mapping Point Clouds

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B3, 301–307. https://doi.org/10.5194/isprs-archives-XLI-B3-301-2016
Krämer, M., & Senner, J.
(2015)

A modular software architecture for processing of big geospatial data in the cloud

Computers & Graphics, 49, 69–81. https://doi.org/10.1016/j.cag.2015.02.005

Presentations

Innovative topics around Steep presented at various events.

Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides

A Scalable AI Training Platform for Remote Sensing Data

Hendrik M. Würz
Hendrik presented these slides at the AGILE 2023 conference in Delft. They show our work on a scalable platform for AI training based on the example of remote sensing data.
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides

Dynamic Workflow Execution in the Cloud using Steep

Hendrik M. Würz
This presentation was held by Hendrik in Berlin in 2022 as a guest lecture organized by the collaborative research center FONDA - Foundations of Workflows for Large-Scale Scientific Data Analysis.
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides
Slides

Capability-based scheduling of scientific workflows in the cloud

Michel Krämer
Michel presented his paper “Capability-based scheduling of scientific workflows in the cloud” at the DATA conference 2020. He talked about Steep’s software architecture and its scheduling algorithm that assigns process chains to virtual machines.