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- automation (6) (remove)
Planetary research is often user-based and requires considerable skill, time, and effort. Unfortunately, self-defined boundary conditions, definitions, and rules are often not documented or not easy to comprehend due to the complexity of research. This makes a comparison to other studies, or an extension of the already existing research, complicated. Comparisons are often distorted, because results rely on different, not well defined, or even unknown boundary conditions. The purpose of this research is to develop a standardized analysis method for planetary surfaces, which is adaptable to several research topics. The method provides a consistent quality of results. This also includes achieving reliable and comparable results and reducing the time and effort of conducting such studies. A standardized analysis method is provided by automated analysis tools that focus on statistical parameters. Specific key parameters and boundary conditions are defined for the tool application. The analysis relies on a database in which all key parameters are stored. These databases can be easily updated and adapted to various research questions. This increases the flexibility, reproducibility, and comparability of the research. However, the quality of the database and reliability of definitions directly influence the results. To ensure a high quality of results, the rules and definitions need to be well defined and based on previously conducted case studies. The tools then produce parameters, which are obtained by defined geostatistical techniques (measurements, calculations, classifications). The idea of an automated statistical analysis is tested to proof benefits but also potential problems of this method. In this study, I adapt automated tools for floor-fractured craters (FFCs) on Mars. These impact craters show a variety of surface features, occurring in different Martian environments, and having different fracturing origins. They provide a complex morphological and geological field of application. 433 FFCs are classified by the analysis tools due to their fracturing process. Spatial data, environmental context, and crater interior data are analyzed to distinguish between the processes involved in floor fracturing. Related geologic processes, such as glacial and fluvial activity, are too similar to be separately classified by the automated tools. Glacial and fluvial fracturing processes are merged together for the classification. The automated tools provide probability values for each origin model. To guarantee the quality and reliability of the results, classification tools need to achieve an origin probability above 50 %. This analysis method shows that 15 % of the FFCs are fractured by intrusive volcanism, 20 % by tectonic activity, and 43 % by water & ice related processes. In total, 75 % of the FFCs are classified to an origin type. This can be explained by a combination of origin models, superposition or erosion of key parameters, or an unknown fracturing model. Those features have to be manually analyzed in detail. Another possibility would be the improvement of key parameters and rules for the classification. This research shows that it is possible to conduct an automated statistical analysis of morphologic and geologic features based on analysis tools. Analysis tools provide additional information to the user and are therefore considered assistance systems.
We and AI
(2021)
Robotic Process Automation (RPA) steht für die softwareunterstützte Bedienung von Softwarelösungen über deren Benutzeroberfläche. Das primäre Ziel, das mit RPA erreicht werden soll, ist die automatisierte Ausführung von Routineaufgaben, die bisher einen menschlichen Eingriff erforderten. Das Potenzial von RPA, Prozesse langfristig zu verbessern, ist allerdings stark begrenzt. Die Automatisierung von Prozessen und die Überbrückung von Medienbrüchen auf der Front-End-Ebene führt zu einer Vielzahl von Abhängigkeiten und Bedingungen, die in diesem Beitrag zusammengefasst werden. Der Weg zu einer nachhaltigen Unternehmensarchitektur (bestehend aus Prozessen und Systemen) erfordert offene, adaptive Systeme mit moderner Architektur, die sich durch ein hohes Maß an Interoperabilität auf verschiedenen Ebenen auszeichnen.
It’s personal
(2021)
The new technologies of the Fourth Industrial Revolution (4IR) are disrupting traditional models of work and learning. While the impact of digitalization on education was already a point of serious deliberation, the COVID-19 pandemic has expedited ongoing transitions. With 90% of the world’s student population having been impacted by national lockdowns—online learning has gone from being a luxury to a necessity, in a context where around 3.6 billion people are offline. As the impacts of the 4IR unfold alongside the current crisis, it is not enough for future policy pathways to prioritize educational attainment in the traditional sense; it is essential to reimagine education itself as well as its delivery entirely. Future policy narratives will need to evaluate the very process of learning and identify the ways in which technology can help reduce existing disparities and enhance digital access, literacy and fluency in a scalable manner. In this context, this chapter analyses the status quo of online learning in India and Germany. Drawing on the experiences of these two economies with distinct trajectories of digitalization, the chapter explores how new technologies intersect with traditional education and local sociocultural conditions. Further, the limitations and opportunities presented by dominant ed-tech models is critically analyzed against the ongoing COVID-19 pandemic.
In a warming Arctic, permafrost-related disturbances, such as retrogressive thaw slumps (RTS), are becoming more abundant and dynamic, with serious implications for permafrost stability and bio-geochemical cycles on local to regional scales. Despite recent advances in the field of earth observation, many of these have remained undetected as RTS are highly dynamic, small, and scattered across the remote permafrost region. Here, we assessed the potential strengths and limitations of using deep learning for the automatic segmentation of RTS using PlanetScope satellite imagery, ArcticDEM and auxiliary datasets. We analyzed the transferability and potential for pan-Arctic upscaling and regional cross-validation, with independent training and validation regions, in six different thaw slump-affected regions in Canada and Russia. We further tested state-of-the-art model architectures (UNet, UNet++, DeepLabv3) and encoder networks to find optimal model configurations for potential upscaling to continental scales. The best deep learning models achieved mixed results from good to very good agreement in four of the six regions (maxIoU: 0.39 to 0.58; Lena River, Horton Delta, Herschel Island, Kolguev Island), while they failed in two regions (Banks Island, Tuktoyaktuk). Of the tested architectures, UNet++ performed the best. The large variance in regional performance highlights the requirement for a sufficient quantity, quality and spatial variability in the training data used for segmenting RTS across diverse permafrost landscapes, in varying environmental conditions. With our highly automated and configurable workflow, we see great potential for the transfer to active RTS clusters (e.g., Peel Plateau) and upscaling to much larger regions.
Self-adaptive data quality
(2017)
Carrying out business processes successfully is closely linked to the quality of the data inventory in an organization. Lacks in data quality lead to problems: Incorrect address data prevents (timely) shipments to customers. Erroneous orders lead to returns and thus to unnecessary effort. Wrong pricing forces companies to miss out on revenues or to impair customer satisfaction. If orders or customer records cannot be retrieved, complaint management takes longer. Due to erroneous inventories, too few or too much supplies might be reordered.
A special problem with data quality and the reason for many of the issues mentioned above are duplicates in databases. Duplicates are different representations of same real-world objects in a dataset. However, these representations differ from each other and are for that reason hard to match by a computer. Moreover, the number of required comparisons to find those duplicates grows with the square of the dataset size. To cleanse the data, these duplicates must be detected and removed. Duplicate detection is a very laborious process. To achieve satisfactory results, appropriate software must be created and configured (similarity measures, partitioning keys, thresholds, etc.). Both requires much manual effort and experience.
This thesis addresses automation of parameter selection for duplicate detection and presents several novel approaches that eliminate the need for human experience in parts of the duplicate detection process.
A pre-processing step is introduced that analyzes the datasets in question and classifies their attributes semantically. Not only do these annotations help understanding the respective datasets, but they also facilitate subsequent steps, for example, by selecting appropriate similarity measures or normalizing the data upfront. This approach works without schema information.
Following that, we show a partitioning technique that strongly reduces the number of pair comparisons for the duplicate detection process. The approach automatically finds particularly suitable partitioning keys that simultaneously allow for effective and efficient duplicate retrieval. By means of a user study, we demonstrate that this technique finds partitioning keys that outperform expert suggestions and additionally does not need manual configuration. Furthermore, this approach can be applied independently of the attribute types.
To measure the success of a duplicate detection process and to execute the described partitioning approach, a gold standard is required that provides information about the actual duplicates in a training dataset. This thesis presents a technique that uses existing duplicate detection results and crowdsourcing to create a near gold standard that can be used for the purposes above. Another part of the thesis describes and evaluates strategies how to reduce these crowdsourcing costs and to achieve a consensus with less effort.