Fakultät IV - Wirtschaft und Informatik
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Several tools to support autograding of student provided SQL statements have already been introduced. The full potential of such tools can only be leveraged, if they extend beyond grading efficiency by also providing tutoring capabilities to the students. With that, tools become really useful by offering self-paced and individually timed learning experiences. In this paper we present an extension for an SQL autograder which improves the hints generated for students in cases where their solution is not entirely correct. Our approach is to compare the student’s solution with the model solution structurally to identify differences between the syntax trees describing the statements. This complements comparing the student’s query with a model solution based on query results. In addition to improving the quality of hints generated for the students, this concept can also be used easily for data manipulation language (DML) or data definition language (DDL) statements, thus extending the applicability of the autograder. Along with details about the concept we present some example hints generated to illustrate the usefulness of the approach. We also report anecdotally on experiences with the system in two different level database courses. Results from different instances of one of them show improvements of student learning as well as student involvement by using the newly generated hints.
The advancing digitalization of daily life has led to an increasing number of choices in the digital sphere. User interfaces that require either a judgment or a decision, the so-called digital choice environments (DCEs), are essential focal points for interventions to alter behaviors towards individual or societal welfare. However, there is a lack of descriptive and prescriptive knowledge within the field of DCEs. In this research, we follow a multi-stage approach to classify the characteristics of DCEs from a choice-centric viewpoint and disclose configurational trade-offs. To achieve this, we first build a taxonomy of DCEs that we validate through expert interviews. Subsequently, we use cluster analysis to identify four configurations of DCEs, which serve as the basis for the development of a configurational model that outlines configuration-specific user outcomes. Our results contribute to the existing knowledge of digital value creation as well as the explanatory understanding of trade-offs among different DCEs.
Im Kontext der IT-Sicherheit werden Provenance Graphen für die Beantwortung sicherheitsrelevanter Fragen wie „Welche Auswirkungen kann diese Sicherheitslücke haben?“ oder „Welcher Prozess hat meine Daten verändert oder gelöscht? verwendet. Weiterhin können Provenance Graphen für das Training von Machine Learning Methoden für die Erkennung von Angriffen auf Netzwerke und Betriebssysteme genutzt werden. Aufgrund der hohen Datenraten bei der Erzeugung von Provenance Graphen (mindestens 1,2 MB/s in realistischen Szenarien) ist eine effiziente Datenverwaltung für den praktischen Einsatz dieser Graphen notwendig.
In dieser Arbeit wird untersucht, welche Anforderungen und Herausforderungen für die Verwaltung von Provenance Graphen existieren und welche Vor- und Nachteile sich aus der Verwaltung mit den Datenbankmanagementsystemen (DBMS) PostgreSQL, Neo4j, ONgDB und Memgraph ergeben.
Zur Untersuchung wurde ein Versuchsaufbau für reproduzierbare Experimente entwickelt, in dem ein Provenance Graph Datensatz in die untersuchten DBMS geschrieben wird und dabei Hardwaremetriken und Query-Antwortzeiten zur späteren Auswertung erhoben und gespeichert werden.
Die Ergebnisse der Arbeit zeigen, dass die hier betrachteten DBMS nur teilweise die recherchierten Anforderungen einer Datenverwaltung für Provenance Graphen im Kontext der IT-Sicherheit erfüllen können. Es konnte gezeigt werden, dass sich relationale Datenbanken durch sparsameren Ressourcenverbrauch gegenüber Graphdatenbanken auszeichnen, dafür aber Nachteile in den Abfragemöglichkeiten durch die Querysprache aufweisen.
In vielen Anwendungskontexten spielen Netzwerke aus Entitäten (Personen, Orte, abstrakte Objekte) und Beziehungen zwischen Entitäten eine wichtige Rolle, die mathematisch als Graphen betrachtet werden können. Die Visualisierung solcher Netzwerke – engl. Graph Drawing – erleichtert es dem Menschen, diese zu verstehen und beispielsweise Muster zu erkennen. Im folgenden Beitrag werden Anwendungen aus der Bioinformatik und der IT-Sicherheit mit ihren jeweiligen Herausforderungen vorgestellt. Insbesondere wird darauf eingegangen, wie diese Herausforderungen, etwa die Dynamik eines Netzwerkes, die Wahl der visuellen Mittel bestimmen.
In der Bachelorarbeit wird ein Document-Retrieval-System zur Suche von geeigneten Kandidaten anhand von Lebensläufen (CVs) entwickelt. Das System soll die CVs dabei nach semantischer Ähnlichkeit zu der Anfrage suchen und nicht nach dem Vorkommen der Anfragewörter. Dafür werden Large Language Models verwendet, die mit ihrer erlernten Sprachfähigkeit neben generativen Aufgaben auch Texte semantisch in Embeddings abbilden können.
As digital technologies advance, user experience (UX) has become crucial for software and services success. The User Experience Questionnaire Plus (UEQ+) is a flexible tool used to evaluate UX through questionnaires tailored to specific problems, yet a critical factor often overlooked is Trust. Trust, understood as a user’s belief in a software’s ability to function consistently, securely, and with respect for user data privacy, is especially pivotal in areas like financial services, health informatics, and e-commerce platforms. This paper focuses on the construction and validation of Trust as a new factor in the UEQ+. During the construction phase, an initial collection of potential items was assembled for the trust factor. A subsequent study involving 405 participants facilitated the reduction of these items to four, a task accomplished via factor analysis. The proceeding stages involved two additional validation phases, enlisting a total of 897 participants, wherein the selected items were subject to validation. The culmination of this process resulted in a newly validated factor, Trust, which is constituted by the following items: insecure-secure, untrustworthy-trustworthy, unreliable-reliable, and non-transparent-transparent.
As collaborative technologies become integral in both professional and leisurely settings, especially during the rise of remote work and digital communities due to COVID-19, understanding the user experience (UX) factors is critical. This study aims to explore the differential importance of these UX factors across professional and leisure contexts, leveraging the widespread use of collaboration tools for an in-depth analysis. The objective of the study is to identify and assess key UX factors in collaboration tools, and to quantify their differential impact in professional and leisure settings. Our research underscores the nuanced role of context in evaluating User Experience (UX) factors’ importance in collaboration tools, with significant variances observed across professional and leisure settings. While some UX factors, including accessibility, clarity, and intuitive use, maintained universal importance across contexts and tools, others—specifically dependability and efficiency—contradicted assumptions of being universal" hygiene factors", demonstrating the complexity of UX evaluations. This complexity necessitates a differentiated approach for each context and collaboration tool type, challenging the possibility of a singular evaluation or statement.
To enable an interactive product to provide adequate user experience (UX), it is important to ensure the quantitative measurability of this parameter. The User Experience Questionnaire (UEQ) is a well-known and popular method for such a UX measurement. One of the key features of this questionnaire is a benchmark that helps to interpret measurement results by a comparison with a large dataset of results obtained for other products. For situations where filling out the entire UEQ is too time-consuming, there is a short version (UEQ-S). However, there is currently no sufficient data available to construct an independent and interpretable benchmark for this short version. This paper examines the efficiency of using a modified version of the existing benchmark of the full UEQ for this purpose. The paper also presents some additional evaluation results concerning the UEQ-S.
Context: Software development companies use Agile methods to develop their products or services efficiently and in a goal-oriented way. But this alone is not enough to satisfy user demands today. It is much more important nowadays that a product or service should offer a great user experience — the user wants to have some positive user experience while interacting with the product or service.
Objective: An essential requirement is the integration of user experience methods in Agile software development. Based on this, the development of positive user experience must be managed. We understand management in general as a combination of a goal, a strategy, and resources. When applied to UX, user experience management consists of a UX goal, a UX strategy, and UX resources.
Method: We have conducted a systematic literature review (SLR) to analyse suitable approaches for managing user experience in the context of Agile software development.
Results: We have identified 49 relevant studies in this regard. After analysing the studies in detail, we have identified different primary approaches that can be deemed suitable for UX management. Additionally, we have identified several UX methods that are used in combination with the primary approaches.
Conclusions: However, we could not identify any approaches that directly address UX management. There is also no general definition or common understanding of UX management. To successfully implement UX management, it is important to know what UX management actually is and how to measure or determine successful UX management.
This article looks at a proposed list of generalized requirements for a unified modelling of event processing networks (EPNs) and its application to Amazon Kinesis Data Analytics. It enhances our previous work in this area, in which we recently analyzed Apache Storm and earlier also the EPiA model, the BEMN model, and the RuleCore model. Our proposed EPN requirements look at both: The logical model of EPNs and the concrete technical implementation of them. Therefore, our article provides requirements for EPN models based on attributes derived from event processing in general as well as existing models. Moreover, as its core contribution, our article applies those requirements by an in depth analysis of Amazon Kinesis Data Analytics as a concrete implementation foundation of an EPN model.
