Self-tuning is a feature of autonomic databases that includes the problem of automatic schema design. It aims at providing an optimized schema that increases the overall database performance. While in relational databases automatic schema design focuses on the automated design of the physical schema, in NoSQL databases all levels of representation are considered: conceptual, logical, and physical. This is mainly because the latter are mostly schema-less and lack a standard schema design procedure as is the case for SQL databases. In this work, we carry out a systematic literature survey on automatic schema design in both SQL and NoSQL databases. We identify the levels of representation and the methods that are used for the schema design problem, and we present a novel taxonomy to classify and compare different schema design solutions. Our comprehensive analysis demonstrates that, despite substantial progress that has been made, schema design is still a developing field and considerable challenges need to be addressed, notably for NoSQL databases. We highlight the most important findings from the results of our analysis and identify areas for future research work.

Data-driven methods and data science are important scientific methods in many research fields. All data science approaches require professional data engineering components. At the moment, computer science experts are needed for solving these data engineering tasks. Simultaneously, scientists from many fields (like natural sciences, medicine, environmental sciences, and engineering) want to analyse their data autonomously. The arising task for data engineering is the development of tools that can support an automated data curation and are utilisable for domain experts. In this article, we will introduce four generations of data engineering approaches classifying the data engineering technologies of the past and presence. We will show which data engineering tools are needed for the scientific landscape of the next decade.

When NoSQL database systems are used in an agile software development setting, data model changes occur frequently and thus, data is routinely stored in different versions. The management of versioned data leads to an overhead potentially impeding the software development. Several data migration strategies exist that handle legacy data differently during data accesses, each of which can be characterized by certain advantages and disadvantages. Depending on the requirements for the software application, we evaluate and compare different migration strategies through metrics like migration costs and latency as well as precision and recall. Ideally, exactly that strategy should be selected whose characteristics fulfill service-level agreements and match the migration scenario, which depends on the query workload and the changes in the data model which imply an evolution of the database schema. In this paper, we present a methodology of self-adapting data migration, which automatically adjusts migration strategies and their parameters with respect to the migration scenario and service-level agreements, thereby contributing to the self-management of database systems and supporting agile development.

The age of digitization has led to a significant increase in the amount and variety of data, particularly within the research domain, where data previously stored in paper form has now been digitized and integrated into research management processes. The rapid growth of Big Data, driven by technologies like the Internet of Things, presents challenges for conventional data processing methods. However, data alone, stored in silos, lacks value. To unlock its potential, data must be analysed and processed to generate insights and predictions that enable evidence-based decision-making. Predictive Analytics (PA) is a powerful tool for this purpose. By leveraging PA and advanced statistical methods, predictive models for research management can be developed, helping to forecast research trends and outcomes, which in turn, provides decision-makers with a reliable, forward-looking basis for strategic decisions in research management. This paper explores the application of PA in Current Research Information Systems (CRIS) to enhance decision-making. A case study using metadata from 20,000 publications indexed in Scopus demonstrates how PA can identify emerging research topics and predict future trends. Machine learning algorithms such as Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest, and Tree classifiers were employed, with metrics such as Area Under ROC (AUC), classification accuracy (CA), F1-score, precision, and recall evaluated. The results indicate that the kNN algorithm provided the highest performance with an AUC of 0.451 and a classification accuracy of 87.4%. These results show that predictive models can reveal significant patterns in research data, supporting data-driven decisionmaking for research management. Additionally, the study applied Latent Semantic Indexing (LSI) and clustering techniques to identify and categorize key topics within the data, showing a thematic focus on areas such as smart cities and urban intelligence before predictions, and CRIS applications after predictions. The findings illustrate how PA can optimize research management by identifying gaps in research and forecasting emerging topics, thereby aiding institutions in making more informed, evidencebased decisions

The use of Elastic Stack (ELK) solutions and Knowledge Graphs (KGs) has attracted a lot of attention lately, with promises of vastly improving business performance based on new business insights and better decisions. This allows organizations not only to reap the ultimate benefits of data governance but also to consider the widest possible range of relevant information when deciding their next steps. In this paper, we examine how data management and data visualization are used in organizations that use ELK solutions to collect integrated data from different sources in one place and visualize and analyze them in near-real time. We also present some interpretable Knowledge Graphs, GRAPHYP, which are innovative by processing an analytical information geometry and can be used together with an ELK to improve data quality and visualize the data to make informed decisions in organizations. Good decisions are the backbone of successful organizations. Ultimately, this research is about integrating a combined solution between ELK and SKG GRAPHYP and showing users the advantages in this area.