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.

Nowadays, the cities continuously attract new residents and become preferable places to live. They provide better quality services in transportation, healthcare and education compared to villages. The cultural diversity and tourist attractiveness of the cities is also greater than the rural areas. At the same time, due to urbanization the cities meet a lot of challenges affecting both their economic performance and well-being. Some of them, like higher prices of services and goods might be directly measured, and others, such as pollution, traffic congestion and limited parking spaces, are difficult to quantify in term of cost. This paper examines the research efforts towards establishing a framework of indicators to monitor and assess the performance and sustainability of smart cities. It presents an analysis of the currently available smart city indicators (SCIs) by classifying them in six thematic areas, namely Smart Nature, Smart Living, Smart Mobility, Smart Governance, Smart People and Smart Economy.

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.

Testing of Service-Oriented Architecture (SOA) implementations brings various challenges at infrastructure, service as well as orchestration level. In order to address these challenges the TASSA framework was developed for testing of web service orchestrations. The paper is focused on one of the TASSA framework tools, named TESSI (TESting of Service Implementations), providing test case generation, execution and management. The core functionality of the TESSI tool includes generation of SOAP request templates, definition of assertions at HTTP, SOAP and BPEL variable levels, support of data driven testing, test execution and management. Its first prototype is implemented as an open source plug-in for NetBeans IDE.