The ubiquitous availability of datasets has spurred the utilization of Artificial Intelligence methods and models to extract valuable insights, unearth hidden patterns, and predict future trends. However, the current process of data collection and linking heavily relies on expert knowledge and domain-specific understanding, which engenders substantial costs in terms of both time and financial resources. Therefore, streamlining the data acquisition, harmonization, and enrichment procedures to deliver high-fidelity datasets readily usable for analytics is paramount. This paper explores the capabilities of SemTUI, a comprehensive framework designed to support the enrichment of tabular data by leveraging semantics and user interaction. Utilizing SemTUI, an iterative and interactive approach is proposed to enhance the flexibility, usability and efficiency of geospatial data enrichment. The approach is evaluated through a pilot case study focused on urban planning, with a particular emphasis on geocoding. Using a real-world scenario involving the analysis of kindergarten accessibility within walking distance, the study demonstrates the proficiency of SemTUI in generating precise and semantically enriched location data. The incorporation of human feedback in the enrichment process successfully enhances the quality of the resulting dataset, highlighting SemTUI’s potential for broader applications in geospatial analysis and its usability for users with limited expertise in manipulating geospatial data.

Maintaining vital societal functions requires essential systems and assets of critical infrastructure. In this context, the city of Sofia faces a compelling challenge in providing sufficient kindergarten facilities for the citizens. This paper leverages a spatial analysis approach inspired by the 15-minute city concept to support decision-making in urban digital twin solutions. It utilizes a graph-based algorithm to efficiently allocate residential buildings to kindergartens within a 1200-meter radius, considering capacity limitations. The findings offer crucial insights for urban planning, emphasizing the intricate balance between supply and demand for essential amenities in the ever-evolving urban environment.The study underscores the significant potential of spatial analysis tools like GIS and innovative approaches to enhance urban living conditions and advance sustainable urban development. It showcases the achievement of 99.5% capacity utilization by the spatial allocation algorithm, all while acknowledging its reliance on specific data and assumptions. Future work encompasses improving data precision, optimizing advanced algorithms, and integrating public transport data to enhance urban accessibility further.

Data enrichment facilitates the creation of rich, expressive, and high-quality datasets, enabling valuable analytics and enhanced decision-making. The accurate geolocation of residential addresses and travel routes is crucial for determining the most appropriate locations of critical social infrastructure, such as educational and medical centres. This paper introduces an interactive semantic enrichment approach that enhances urban data by integrating high-quality geospatial information. The approach is supported by a modular and extensible data enrichment framework, which leverages existing geolocation services, enabling seamless data integration. Human-in-the-loop revision is employed to enhance the quality of geocoding results. A real-world pilot study conducted in Sofia, Bulgaria, was used to validate this approach, demonstrating its promising potential in addressing pressing issues in parametric urban planning.

Quality of Service (QoS) assessment and prediction are an important issue in the development of distributed applications. The provision of web services with the same functionality gives rise to a need for implementation of more precise web service selection procedures. The paper introduces a new approach for web service selection that takes into account the web services' varying behavior defined by their constantly changing QoS properties as well as the ambiguous clients' QoS requirements. These uncertainties are formalized using respectively the probability theory and the theory of fuzzy sets. The feasibility of the approach is proved through a real scenario for web service selection.