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In dem vorliegenden Beitrag setzt sich der Verfasser mit dem Urteil des EuGH vom 4.5.2023 (Az.: C-60/22, DSB 2023, 178) zu den Auswirkungen eines formellen Verstoßes des Verantwortlichen gegen die Pflichten aus Artt. 26, 30 DSGVO (juris: EUV 2016/679) auf die Rechtmäßigkeit der Datenverarbeitung auseinander. Nachdem zunächst der zugrunde liegende Sachverhalt und der Hintergrund des Vorlageverfahrens skizziert wurden, gibt der Verfasser einen Überblick über die wesentlichen Entscheidungsgründe des EuGH. Insbesondere stelle der EuGH hier fest, dass die Rechtmäßigkeit der Verarbeitung in Art. 6 DSGVO geregelt sei und sich eine rechtswidrige Verarbeitung daher nur aus einem Verstoß gegen die Artt. 6 ff. DSGVO ergeben könne; die Pflichten aus Art. 26 und Art. 30 DSGVO würden nicht zu den Gründen für die Rechtmäßigkeit der Verarbeitung zählen. Mit Blick auf die Praxis lasse sich, so der Verfasser abschließend, festhalten, dass die Entscheidung insofern nicht überraschend sei; jedoch sei die Feststellung, dass sich aus Verstößen gegen Art. 26 und Art. 30 DSGVO kein Verstoß gegen das Grundrecht auf den Schutz personenbezogener Daten nachweisen lasse überraschend und bedenklich. Auch überrasche es, dass der EuGH eher in einem Nebensatz feststelle, dass der Verantwortliche im Prozess aufgrund seiner Rechenschaftspflicht gegenüber Betroffenen beweisbelastet ist; ob sich die Kammer hier der möglichen Auswirkungen ihrer Ausführungen bewusst gewesen sei, bleibe fraglich.
Umsatzbasierte Bußgelder – wie sonst nur aus dem Kartellrecht bekannt – waren einer der Gründe, warum die Datenschutz-Grundverordnung (DSGVO) vor ihrem Inkrafttreten für erhebliches Aufsehen sorgte. Die vielfach relevanteren Schadensersatzansprüche, die, wie bei „Dieselgate“, aufgrund der Vielzahl von betroffenen Personen und der aus Sicht von Rechtsdienstleistern bestehenden Skalierbarkeit mit weitaus höheren Einbußen für Unternehmen einhergehen können, blieben zunächst unbeachtet. Inzwischen ist der Schadensersatzanspruch gem. Art. 82 DSGVO die Vorschrift, die die meisten Vorlagen zum Europäischen Gerichtshof (EuGH) der letzten Jahre hervorgerufen hat. Am 4.5.2023 hat nun der EuGH (Urteil v. 4.5.2023 - Rs. C-300/21, NWB GAAAJ-41389) in einem Grundsatzurteil über zentrale Fragen rund um den Ersatz immaterieller Schäden als Folge von Datenschutzverstößen entschieden.
Extracting workflow nets from textual descriptions can be used to simplify guidelines or formalize textual descriptions of formal processes like business processes and algorithms. The task of manually extracting processes, however, requires domain expertise and effort. While automatic process model extraction is desirable, annotating texts with formalized process models is expensive. Therefore, there are only a few machine-learning-based extraction approaches. Rule-based approaches, in turn, require domain specificity to work well and can rarely distinguish relevant and irrelevant information in textual descriptions. In this paper, we present GUIDO, a hybrid approach to the process model extraction task that first, classifies sentences regarding their relevance to the process model, using a BERT-based sentence classifier, and second, extracts a process model from the sentences classified as relevant, using dependency parsing. The presented approach achieves significantly better resul ts than a pure rule-based approach. GUIDO achieves an average behavioral similarity score of 0.93. Still, in comparison to purely machine-learning-based approaches, the annotation costs stay low.
Providing healthcare services frequently involves cognitively demanding tasks, including diagnoses and analyses as well as complex decisions about treatments and therapy. From a global perspective, ethically significant inequalities exist between regions where the expert knowledge required for these tasks is scarce or abundant. One possible strategy to diminish such inequalities and increase healthcare opportunities in expert-scarce settings is to provide healthcare solutions involving digital technologies that do not necessarily require the presence of a human expert, e.g., in the form of artificial intelligent decision-support systems (AI-DSS). Such algorithmic decision-making, however, is mostly developed in resource- and expert-abundant settings to support healthcare experts in their work. As a practical consequence, the normative standards and requirements for such algorithmic decision-making in healthcare require the technology to be at least as explainable as the decisions made by the experts themselves. The goal of providing healthcare in settings where resources and expertise are scarce might come with a normative pull to lower the normative standards of using digital technologies in order to provide at least some healthcare in the first place. We scrutinize this tendency to lower standards in particular settings from a normative perspective, distinguish between different types of absolute and relative, local and global standards of explainability, and conclude by defending an ambitious and practicable standard of local relative explainability.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
The popularity of social media and particularly Instagram grows steadily. People use the different platforms to share pictures as well as videos and to communicate with friends. The potential of social media platforms is also being used for marketing purposes and for selling products. While for Facebook and other online social media platforms the purchase decision factors are investigated several times, Instagram stores remain mainly unattended so far. The present research work closes this gap and sheds light into decisive factors for purchasing products offered in Instagram stores. A theoretical research model, which contains selected constructs that are assumed to have a significant influence on Instagram user´s purchase intention, is developed. The hypotheses are evaluated by applying structural equation modelling on survey data containing 127 relevant participants. The results of the study reveal that ‘trust’, ‘personal recommendation’, and ‘usability’ significantly influences user’s buying intention in Instagram stores.
Im Handel mit Kraftfahrzeugen gehören Aspekte des gutgläubigen Erwerbs zu den beinahe alltäglichen Standardproblemen. Der BGH fügt in seiner Entscheidung v. 23.9.2022–VZR148/21, MDR 2022, 1541 diesem im Detail breit gefächerten Themenfeld einen weiteren Mosaikstein hinzu: Der Erwerber erhielt das verkaufte Kfz ohne Übergabe einer Zulassungsbescheinigung Teil II, behauptet aber, diese Bescheinigung sei dem vom ihm eingeschalteten Vermittler bei Erwerb (als Fälschung) vorgelegt worden. Tatsächlich befand sich das Original durchgängig beim wahren Eigentümer, der nunmehr Herausgabe des Fahrzeugs verlangt. Der BGH schützt in dieser Gestaltung im Ergebnis den Erwerber. Die Entscheidung ist in mehrfacher Hinsicht bemerkenswert.
Dieser verständliche Einstieg in SAP S/4HANA führt Sie anhand des Beispielunternehmens Global Bike durch die zentralen Abläufe in Vertrieb, Einkauf, Rechnungswesen, Produktion und Lagerverwaltung. Sie werden mit den betriebswirtschaftlichen Grundlagen, den relevanten Organisationsstrukturen und Stammdaten sowie den Prozessen vertraut gemacht. Mithilfe von Praxisbeispielen und Fallstudien sind Sie schon bald SAP-S/4HANA-Profi – für mehr Erfolg in Studium und Beruf!
Purpose
In the determination of the measurement uncertainty, the GUM procedure requires the building of a measurement model that establishes a functional relationship between the measurand and all influencing quantities. Since the effort of modelling as well as quantifying the measurement uncertainties depend on the number of influencing quantities considered, the aim of this study is to determine relevant influencing quantities and to remove irrelevant ones from the dataset.
Design/methodology/approach
In this work, it was investigated whether the effort of modelling for the determination of measurement uncertainty can be reduced by the use of feature selection (FS) methods. For this purpose, 9 different FS methods were tested on 16 artificial test datasets, whose properties (number of data points, number of features, complexity, features with low influence and redundant features) were varied via a design of experiments.
Findings
Based on a success metric, the stability, universality and complexity of the method, two FS methods could be identified that reliably identify relevant and irrelevant influencing quantities for a measurement model.
Originality/value
For the first time, FS methods were applied to datasets with properties of classical measurement processes. The simulation-based results serve as a basis for further research in the field of FS for measurement models. The identified algorithms will be applied to real measurement processes in the future.
Many of today’s factors make software development more and more complex, such as time pressure, new technologies, IT security risks, et cetera. Thus, a good preparation of current as well as future software developers in terms of a good software engineering education becomes progressively important. As current research shows, Competence Developing Games (CDGs) and Serious Games can offer a potential solution.
This paper identifies the necessary requirements for CDGs to be conducive in principle, but especially in software engineering (SE) education. For this purpose, the current state of research was summarized in the context of a literature review. Afterwards, some of the identified requirements as well as some additional requirements were evaluated by a survey in terms of subjective relevance.