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Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference for tabular data with explainable AI (XAI). This third version was derived from two earlier review protocols.
Data engineering is an integral part of the data science process. It comprises tasks such as data ingestion, data transformation, and data quality assurance. In order to fulfill these tasks, schema inference is an important capability. Its goal is to detect the structure of a dataset and to derive metadata on hierarchies, data types, etc. Artificial intelligence (AI) has the potential to automate schema inference and thus increase the efficiency of the data science process. However, as government institutions are subject to special regulations, explainability of AI models can be a mandatory requirement. Goal of this research protocol is to plan a systematic review of literature on schema inference with explainable AI (XAI) for data engineering in government institutions.
If we believe Edward Snowden, encryption is "the only true protection against surveillance". However, advances in quantum technology might endanger this safeguard. Our article discusses why quantum computing poses a threat to data security and what to do about it. Instead of a purely theoretical analysis, we build on code examples using Python, C, and Linux.
Data engineering makes up a large part of the data science process. In CRISP-DM this process stage is called "data preparation". It comprises tasks such as data ingestion, data transformation and data quality assurance. In our article we solve typical data engineering tasks using ChatGPT and Python. By doing so, we explore the link between data engineering and the new discipline of prompt engineering.
Vorlaufforschung 2021
(2023)
Liebe Leser*innen,
ein ansehnlicher Band mit wissenschaftlichen Untersuchungen aus ganz unterschiedlichen Fachgebieten liegt vor Ihnen und lädt zum darin Schmökern und zum Verweilen bei dem einen oder anderen Projekt ein.
14 Projekte konnten im Jahr 2021 gefördert werden und wieder zeigen die hier veröffentlichten 11 Ergebnisberichte sowohl die thematische Breite unserer Hochschule als auch die wissenschaftliche Tiefe, mit der die einzelnen Akteure unterwegs sind. Der Klimawandel und die Pandemie spielen bei den Untersuchungen genauso eine Rolle wie Künstliche Intelligenz und die Untersuchung von Werkstoffen, wie zum Beispiel bioaktivem Glas, klebrigen Schmierfetten oder menschlichen Knochen (...).
[aus Grußwort: Tilman Botsch]
Lehre an Hochschulen gemeinsam denken. Motivierte Akteure in kreativer Atmosphäre an einen Tisch bringen. Kommunikation auf Augenhöhe. Das ist das Lehrlabor³. Mit diesem Programm wurde erstmals in Bayern ein Format erprobt, in dem über einen Zeitraum von sieben Monaten innovative Lehr- und Lernsettings in rollenübergreifenden Hochschulteams gestaltet wurden. Forschend und zugleich spielerisch entstanden neue Ideen. Insgesamt fünf Hochschulen wurden auf eine Reise mit Ziel „Innovative Lehrentwicklung“ geschickt. Das erste Ergebnis halten Sie in den Händen. Das Lehrlabor³ hat einen ko-kreativen Rahmen für Lehrentwicklung geschaffen und eine Community ins Leben gerufen, die Hochschulgrenzen und Silodenken überwindet und als interdisziplinäre Ideenschmiede neue Impulse für Lehre und Lernen liefert. Auf den folgenden Seiten zeigt Ihnen das Lehrlabor³-Team Ausschnitte dieses Weges. Lassen Sie sich vom Ideenreichtum der Hochschulteams inspirieren und von der Reiselust anstecken.
(Vorw. Frau Prof. Christina Zitzmann)
In 2021, together with fellow scientists, we conducted a survey at the German Chaos Computer Club (CCC). Our goal was to find out which programming languages are most commonly used by hackers. This article follows up the survey and compares its findings with an analysis of the Exploit Database. Readers get step-by-step instructions on how to set up the analysis environment and a summary of the results. Thus, the article not only covers applied techniques, but also offers insights into the world of cybersecurity.
There are numerous articles about the programming languages most commonly used by hackers. Among them, however, there are hardly any scientific studies. One reason might be that hackers mainly operate anonymously and are difficult to reach. This paper aims to shed light on this interesting and relevant research question. In order to find answers, we conducted a survey among the members of the German Chaos Computer Club. As one of the world's largest organisations for information security and hacking, the club provides a good basis for our study. We examine the question of which programming languages are used by hackers as well as the importance of the programming language for their work. The paper offers first insights into the topic and can provide a starting point for further research.