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Diese Arbeit stellt die Entwicklung einer Echtzeit-basierten Recommendation Engine für ein standortbasiertes soziales Netzwerk vor. Die Architektur kombiniert Kafka und Flink zur Echtzeitverarbeitung von Nutzerinteraktionen und Standortdaten. Zusätzlich wird eine Geodatenbank mit OpenStreetMap-Daten verwendet, um Sehenswürdigkeiten effizient zu verwalten. Die vorgestellte Gesamtarchitektur stellt sicher, dass die Recommendation Engine nicht nur die Vorteile von Echtzeit-Stream-Processing nutzt, sondern auch nahtlos in die Infrastruktur einer mobilen App integriert werden kann. Dies ermöglicht eine flexible und skalierbare Lösung, die leicht an wachsende Nutzerzahlen und Datenmengen angepasst werden kann. Die Empfehlungsgenerierung erfolgt über eine Stream-Processing-Pipeline, die Präferenzen der Nutzer analysiert und personalisierte Vorschläge in Echtzeit bereitstellt. Zur Evaluation wurde ein User-Simulator entwickelt, der verschiedene Nutzungsszenarien abbildet. Die Ergebnisse zeigen, dass das System eine um 10 % höhere Like-Rate im Vergleich zu einem zufallsbasierten Ansatz erreicht und gleichzeitig niedrige Latenzzeiten bei hohem Durchsatz bietet.
In today’s world, data is generated at an unprecedented pace, and our ability to harness it is changing the way we live, work, and even think. Data science, the interdisciplinary field that blends statistics, computer science, and domain-specific knowledge, empowers us to extract insights from this vast ocean of data. As data science becomes increasingly essential across various industries and sectors, there is a growing need for skilled professionals who can make sense of data and transform it into actionable information. This book is designed to give you a very broad and at the same time a very practical hands-on tour through the full spectrum of data science approaches
LC-MS/MS-based untargeted metabolomics is a rapidly developing research field spawning increasing numbers of computational metabolomics tools assisting researchers with their complex data processing, analysis, and interpretation tasks. In this article, we review the entire untargeted metabolomics workflow from the perspective of information visualization, visual analytics and visual data integration. Data visualization is a crucial step at every stage of the metabolomics workflow, where it provides core components of data inspection, evaluation, and sharing capabilities. However, due to the large number of available data analysis tools and corresponding visualization components, it is hard for both users and developers to get an overview of what is already available and which tools are suitable for their analysis. In addition, there is little cross-pollination between the fields of data visualization and metabolomics, leaving visual tools to be designed in a secondary and mostly ad hoc fashion. With this review, we aim to bridge the gap between the fields of untargeted metabolomics and data visualization. First, we introduce data visualization to the untargeted metabolomics field as a topic worthy of its own dedicated research, and provide a primer on cutting-edge visualization research into data visualization for both researchers as well as developers active in metabolomics. We extend this primer with a discussion of best practices for data visualization as they have emerged from data visualization studies. Second, we provide a practical roadmap to the visual tool landscape and its use within the untargeted metabolomics field. Here, for several computational analysis stages within the untargeted metabolomics workflow, we provide an overview of commonly used visual strategies with practical examples. In this context, we will also outline promising areas for further research and development. We end the review with a set of recommendations for developers and users on how to make the best use of visualizations for more effective and transparent communication of results.
Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain
(2024)
The exponential growth of data has outpaced human ability to process information, necessitating innovative approaches for effective human-data interaction. To transform raw data into meaningful insights, storytelling, and visualization have emerged as powerful techniques for communicating complex information to decision makers. This article offers a comprehensive, systematic review of the utilization of storytelling in visualizations. It organizes the existing literature into distinct categories, encompassing frameworks, data and visualization types, application domains, narrative structures, outcome measurements, and design principles. By providing a well-structured overview of this rapidly evolving field, the article serves as a valuable guide for educators, researchers, and practitioners seeking to harness the power of storytelling in data visualization.
Mass spectral libraries have proven to be essential for mass spectrum annotation, both for library matching and training new machine learning algorithms. A key step in training machine learning models is the availability of high-quality training data. Public libraries of mass spectrometry data that are open to user submission often suffer from limited metadata curation and harmonization. The resulting variability in data quality makes training of machine learning models challenging. Here we present a library cleaning pipeline designed for cleaning tandem mass spectrometry library data. The pipeline is designed with ease of use, flexibility, and reproducibility as leading principles.Scientific contributionThis pipeline will result in cleaner public mass spectral libraries that will improve library searching and the quality of machine-learning training datasets in mass spectrometry. This pipeline builds on previous work by adding new functionality for curating and correcting annotated libraries, by validating structure annotations. Due to the high quality of our software, the reproducibility, and improved logging, we think our new pipeline has the potential to become the standard in the field for cleaning tandem mass spectrometry libraries.