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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. This second version includes adjustments resulting from the first iteration of the review.
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.
Die Performance der Kontaktverfolgung, Isolation und Quarantäne bleibt deutlich hinter den Möglichkeiten zurück und trägt zu wenig zur Reduktion der Reproduktionszahlen bei. In Kombination mit einer nachlassenden Mitwirkung der Bevölkerung könnte bald ein Punkt erreicht sein, an dem wir nur noch auf die Impfung warten können. Wir müssen schnellstmöglich auf ein Niveau der Neuinfektionen zurückzukehren, auf dem die Gesundheitsämter einen Beitrag leisten können. Wir brauchen eine Stelle, die für durchgängig (!) pfeilschnelle (!) Abläufe im Gesamtprozess (Test-Zugang, schnellste Auswertung und Mitteilung von Testergebnissen, umgehende Isolierung, Kontaktverfolgung, Testung der Kontakte sowie wirksame Quarantäne.) verantwortlich zeichnet. Dabei sollten Kompetenzen zu Management, Organisation, zeitlichen Abläufen, 24/7-Betrieb, Drive-In-Testing, detektivischer Arbeit, IT-Einsatz und mathematisch basierter Optimierung des Gesamtprozesses im Vordergrund stehen.
Mittels eines modifizierten SEIR-Modells und der Basisreproduktionsrate wird die aktuelle Entwicklung zu COVID 19 simuliert und die weitere Entwicklung in Richtung Containment prognostiziert. Unterschiede zwischen verschiedenen europäischen Staaten und innerhalb Deutschlands werden aufgezeigt. Bezüglich der Fallsterblichkeitszahlen für Deutschland und veröffentlichen Simulationen zur Herdenimmunität werden gravierende mathematische Mängel aufgedeckt. Wege in Richtung einer Exitstrategie werden aufgezeigt.