Abstract The European Health Data Space (EHDS) aims to enable secure, standardized, and cross-border use of health data for both care and research. While maturity models (MMs) are widely applied to support digital transformation in healthcare, their alignment with EHDS-specific legal and strategic goals remains unclear. This study presents a systematic literature review of 37 publications using a keyword analysis and qualitative content analysis based on a custom coding framework aligned with the EHDS Regulation. The results show that while technical aspects such as interoperability and security are frequently addressed, patient centered critical legal dimensions — such as data sovereignty and privacy — are underrepresented. Similarly, goals related to secondary use, transparency, and cross-border data portability are insufficiently covered. Both the implementation of EHDS principles by healthcare actors and the design of maturity models are hindered by structural, regulatory, and technological barriers. In conclusion, existing maturity models offer only partial support for the EHDS. To bridge this gap, future models must more comprehensively integrate legal compliance, governance, and trust-enabling mechanisms.

Artifact Evaluation (AE) is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cybersecurity, particularly in IoT and CPSs, where large-scale, heterogeneous, and privacy-sensitive data meet safety-critical actuation. Yet, manual reproducibility checks are time-consuming and do not scale with growing submission volumes. In this work, we demonstrate that Large Language Models (LLMs) can provide powerful support for AE tasks: (i) text-based reproducibility rating, (ii) autonomous sandboxed execution environment preparation, and (iii) assessment of methodological pitfalls. Our reproducibility-assessment toolkit yields an accuracy of over 72% and autonomously sets up execution environments for 28% of runnable cybersecurity artifacts. Our automated pitfall assessment detects seven prevalent pitfalls with high accuracy ($F_1$ > 92%). Hence, the toolkit significantly reduces reviewer effort and, when integrated into established AE processes, could incentivize authors to submit higher-quality and more reproducible artifacts. IoT, CPS, and cybersecurity conferences and workshops may integrate the toolkit into their peer-review processes to support reviewers' decisions on awarding artifact badges, improving the overall sustainability of the process.

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Knowledge graphs (KGs) play a crucial role in the integration and organization of heterogeneous data and knowledge, enabling advanced data analytics and decision-making across various industries. This vision paper addresses critical challenges in managing KGs, emphasizing their relevance in integrating information from disparate sources. We propose the concept of knowledge graph ecosystems and life cycles to systematically manage tasks, e.g., data integration, standardization, continuous updates, efficient querying, and provenance tracking. By adopting our approach, organizations can enhance the accuracy, consistency, and reliability of KGs, thus improving knowledge management, enabling the extraction of valuable insights, and ensuring transparency and accountability.

As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance, a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.

S.389-394

Mediated schemas lie at the center of the well recognized data integration architecture. Classical data integration systems rely on a mediated schema created by human experts through an intensive design process. Automatic generation of mediated schemas is still a goal to be achieved. We generate mediated schemas by merging multiple source schemas interrelated by tuple-generating dependencies (tgds). Schema merging is the process to consolidate multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Existing approaches fall short in various aspects, such as restricted expressiveness of input mappings, lacking data level interpretation, the output mapping is not in a logical language (or not given at all), and being confined to binary merging. We present here a novel system which is able to perform native n-ary schema merging using P2P style tgds as input. Suited in the scenario of generating mediated schemas for data integration, the system opts for a minimal schema signature retaining all certain answers of conjunctive queries. Logical output mappings are generated to support the mediated schemas, which enable query answering and, in some cases, query rewriting.

While monitoring and analysis of bio-signals are essential in today's medical diagnostics, it is gaining even more importance with modern computer systems allowing the use of complex real-time algorithms and analysis of huge amounts of data. To ease development of such algorithms, a tool is needed which allows the mobile long-term recording of signals at reasonably high frequencies and at the same time provides a framework for a straightforward implementation of all kinds of real-time monitoring, analysis and visualization algorithms. The Measure tool developed at femu, RWTH Aachen University, consisting of a mobile data logging device and an easily extendable PC software, addresses this problem. The system has been evaluated in a case study analyzing gastrointestinal motility.

Schema merging is the process of consolidating multiple schemas into a unified view. The task becomes particularly challenging when the schemas are highly heterogeneous and autonomous. Classical data integration systems rely on a mediated schema created by human experts through an intensive design process.