Knowledge Graphs (KGs) are powerful tools for organizing and extracting knowledge across various domains. However, KGs scaling in size and complexity make operations such as query answering and machine learning more challenging. These challenges are especially pressing in resource-constrained environments, where sustainability and efficiency become critical design goals, and can be addressed through controlled KG reduction that preserves semantic integrity. In this context, this paper introduces a novel application of approximate computing through controlled entity ablation for efficient KG reduction. We systematically evaluate multiple ablation strategies with different KG embedding techniques, analysing their impact on link prediction accuracy, semantic integrity, and sustainability metrics. The results demonstrate that selective and controlled ablation (of up to 20% of the entities) preserves the core semantic structure of KGs while yielding reductions in energy consumption (of up to 11,5%) and computational overhead (up to 10% savings in total training time). Furthermore, we propose a machine learning-based framework to dynamically assess and optimise ablation strategies based on the KG characteristics and the desired quality thresholds. This work underscores the potential of approximate computing paradigms to provide sustainable solutions that meet the demands of modern data-intensive applications.

Data ecosystems are often used in many social, industrial, and research areas to boost the learning process or enhance the value of the endless amount of data generated and collected daily. Their design principles are consolidated, but today, data ecosystems must address new needs, as they are faced with multiple interdependent challenges, such as users’ engagement, intellectual property, data confidentiality, and data sharing. The achievement of these business objectives highly depends on the deployed policies that govern many, if not all, aspects of the data ecosystems. Identifying the role of these policies in achieving data ecosystems’ objectives is key to their success. This paper analyzes policies and their relations to define how they impact the business objectives of the systems and, possibly, to improve them. The proposed method highlights the interdependencies between different policies. Such results will be leveraged to tune policies to achieve data ecosystems’ business objectives.

Data ecosystems are often the most promising choice to enhance the development of many areas, from industry to social science. Both academic and industry research departments, over time, have refined this technology, formalizing principles, methodologies, and approaches to better handle the initial development overhead in terms of challenges, design, and development. However, there is still space for improvement regarding the countermeasures to keep these projects alive in the long run. These platforms face a significant rate of early-stage failure, primarily due to low user engagement and high cost. Therefore, it is necessary to account for these challenges, particularly in the design and early stages of deployment. The reasons for this phenomenon are already under investigation at the business level, but how to technically account for these phenomena in the design phase and in the technology is still something to be discussed. Therefore, this paper first identifies the main reasons that threaten a long-term use and life of a data ecosystem. Then, discuss the mitigations at the design level and the possible technological solutions. This work moves a step toward a sustainable data ecosystem thanks to the appropriate data governance and technical design choices.

Data Ecosystems (DE) are used across various fields and applications. They facilitate collaboration between organizations, such as companies or research institutions, enabling them to share data and services. A DE can boost research outcomes by managing and extracting value from the increasing volume of generated and shared data in the last decades. However, the adoption of DE solutions for scientific data by R&D departments and scientific communities is still difficult. Scientific data are challenging to manage, and, as a result, a considerable part of this information still needs to be annotated and organized in order to be shared. This work discusses the challenges of employing DE in scientific domains and the corresponding potential mitigations. First, scientific data and their typologies are contextualized, then their unique characteristics are discussed. Typical properties regarding their high heterogeneity and uncertainty make assessing their consistency and accuracy problematic. In addition, this work discusses the specific requirements expressed by the scientific communities when it comes to integrating a DE solution into their workflow. The unique properties of scientific data and domain-specific requirements create a challenging setting for adopting DEs. The challenges are expressed as general research questions, and this work explores the corresponding solutions in terms of data management aspects. Ultimately, the paper presents a real-world scenario with more technical details.