Showcase | Steep

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

AGILE: GIScience Series, 4. https://doi.org/10.5194/agile-giss-4-53-2023

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, I.

(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.

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.

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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.

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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.

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What we’ve created with Steep

Cloud-based platform supporting the fibre roll-out in Germany

Training an AI for forest type classification

Publications

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

A Scalable AI Training Platform for Remote Sensing Data

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

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

Executing cyclic scientific workflows in the cloud

Capability-based Scheduling of Scientific Workflows in the Cloud

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

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

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

Presentations

A Scalable AI Training Platform for Remote Sensing Data

Dynamic Workflow Execution in the Cloud using Steep

Capability-based scheduling of scientific workflows in the cloud