We present an algorithm and a software architecture for a cloud-based system that executes cyclic scientific workflows whose structure may change during run time. Existing approaches either rely on workflow definitions based on directed acyclic graphs (DAGs) or require workarounds to implement cyclic structures. In contrast, our system supports cycles natively, avoids workarounds, and as such reduces the complexity of workflow modelling and maintenance. Our algorithm traverses workflow graphs and transforms them iteratively into linear sequences of executable actions. We call these sequences process chains. Our software architecture distributes the process chains to multiple compute nodes in the cloud and oversees their execution. We evaluate our approach by applying it to two practical use cases from the domains of astronomy and engineering. We also compare it with two existing workflow management systems. The evaluation demonstrates that our algorithm is able to execute dynamically changing workflows with cycles and that design and maintenance of complex workflows is easier than with existing solutions. It also shows that our software architecture can run process chains on multiple compute nodes in parallel to significantly speed up the workflow execution. An implementation of our algorithm and the software architecture is available with the Steep Workflow Management System that we released under an open-source license. The resources for the first practical use case are also available as open source for reproduction.

Abstract. We present the results from evaluating various Convolutional Neural Network (CNN) models to compare their usefulness for forest type classification. Machine Learning based on CNNs is known to be suitable to identify relevant patterns in remote sensing imagery. With the availability of free data sets (e.g. the Copernicus Sentinel-2 data), Machine Learning can be utilized for forest monitoring, which provides useful and timely information helping to measure and counteract the effects of climate change. To this end, we performed a case study with publicly available data from the federal state of North Rhine-Westphalia in Germany. We created an automated pipeline to preprocess and filter this data and trained the CNN models UNet, PSPNet, SegNet, and FCN-8. Since the data contained large rural areas, we augmented the imagery to improve classification results. We reapplied the trained models to the data, compared the results for each model, and evaluated the effect of augmentation. Our results show that UNet performs best with a categorical accuracy of 73% when trained with augmented imagery.

Abstract. We present a cloud-based approach to transform arbitrarily large terrain data to a hierarchical level-of-detail structure that is optimized for web visualization. Our approach is based on a divide-and-conquer strategy. The input data is split into tiles that are distributed to individual workers in the cloud. These workers apply a Delaunay triangulation with a maximum number of points and a maximum geometric error. They merge the results and triangulate them again to generate less detailed tiles. The process repeats until a hierarchical tree of different levels of detail has been created. This tree can be used to stream the data to the web browser. We have implemented this approach in the frameworks Apache Spark and GeoTrellis. Our paper includes an evaluation of our approach and the implementation. We focus on scalability and runtime but also investigate bottlenecks, possible reasons for them, as well as options for mitigation. The results of our evaluation show that our approach and implementation are scalable and that we are able to process massive terrain data.