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The integration of heterogeneous and weakly linked log data poses a major challenge in many log-analytic applications. Knowledge graphs (KGs) can facilitate such integration by providing a versatile representation that can interlink objects of interest and enrich log events with background knowledge. Furthermore, graph-pattern based query languages, such as SPARQL, can support rich log analyses by leveraging semantic relationships between objects in heterogeneous log streams. Constructing, materializing, and maintaining centralized log knowledge graphs, however, poses significant challenges. To tackle this issue, we propose VloGraph—a distributed and virtualized alternative to centralized log knowledge graph construction. The proposed approach does not involve any a priori parsing, aggregation, and processing of log data, but dynamically constructs a virtual log KG from heterogeneous raw log sources across multiple hosts. To explore the feasibility of this approach, we developed a prototype and demonstrate its applicability to three scenarios. Furthermore, we evaluate the approach in various experimental settings with multiple heterogeneous log sources and machines; the encouraging results from this evaluation suggest that the approach can enable efficient graph-based ad-hoc log analyses in federated settings.

Learning styles and affective states influence students’ learning. The purpose of this study is to develop a conceptual framework for identifying and integrating learning styles and affective states of a learner into web-based learning management systems and therefore provide learners with adaptive courses and additional individualized pedagogical guidance that is tailored to their learning styles and affective states. The study was carried out in three phases, the first of which was the investigation and determination of learning styles and affective states which are important for learning. Phase two consisted of the development of an approach for the identification of learning styles and affective states as well as the development of a mechanism to calculate them from the students’ learning interactions within web-based learning management systems. The third phase was to develop a learning strategy that is more personalized and adaptive in nature and tailored to learners’ needs and current situation through considering learners’ learning styles and affective states, aiming to lead to better learning outcomes and progress.

Privacy requirements engineering is a crucial aspect of privacy engineering. It aims to integrate privacy principles into organizational and technical processes throughout the software development lifecycle. This specialized field involves various strategies, including compliance with regulatory frameworks, asset analysis, and system diagram development for threat modeling. The wide range of approaches, while beneficial in providing different perspectives, presents a significant challenge to the novice privacy engineer or developer in identifying the most effective methodologies. The lack of a single methodology highlights the need for a systematic literature review (SLR) to establish a standardized process for privacy requirements engineering that promotes consistency across different methodologies. To address this issue, we conducted a comprehensive SLR to synthesize existing privacy requirements engineering methodologies. Our analysis involved dissecting each method’s processes, tasks, techniques, work products, and resources. Our review examined 40 privacy requirements engineering methodologies detailed in 50 papers, from which we extracted five key processes commonly followed in privacy requirements engineering research. We used this as the foundation for a holistic approach to facilitate the adoption of a comprehensive privacy requirements engineering process. The review also identifies ongoing challenges and suggests future directions in this field.

Privacy requirements engineering acts as a role to systematically elicit privacy requirements from system requirements and legal requirements such as the GDPR. Many methodologies have been proposed, but the majority of them are focused on the waterfall approach, making adopting privacy engineering in agile software development difficult. The other major issue is that the process currently is to a high degree manual. This paper focuses on closing these gaps through the development of a machine learning-based approach for identifying privacy requirements in an agile software development environment, employing natural language processing (NLP) techniques. Our method aims to allow agile teams to focus on functional requirements while NLP tools assist them in generating privacy requirements. The main input for our method is a collection of user stories, which are typically used to identify functional requirements in agile software development. The NLP approach is then used to automate some human-intensive tasks such as identifying personal data and creating data flow diagrams from user stories. The data flow diagram forms the basis for the automatic creation of privacy requirements. Our evaluation shows that our NLP method achieves a fairly good performance in terms of F-Measure. We are also demonstrate the feasibility of our NLP approach in CamperPlus project. Lastly, we are developing a tool to integrate our NLP approach into the privacy requirements engineering pipeline, allowing for manual editing of results so that agile teams can maintain control over the automated approach.

Sharing data within Grid environments can potentially result in losing control over data usage. If the shared information is medical data laws must be followed, enabling tightly controlled, reconstructible access. To comply with these regulations it must be guaranteed, that each data access and modification is recorded to enable audit trails. The provided solution introduces a new concept for virtualized Data Grid environments which consist of a secure virtual machines, harnessing secure data hosting and indemnifying thorough audit trails by logging each data interaction. This solves the problem of traceability and side-access.