One significant digital initiative that is changing Greece’s tax environment is the myDATA platform. The platform, which is a component of the wider digital governance agenda, provides significant added value to enterprises and the tax administration, despite the challenges of adaption. Despite the positive response, we find that the development of the platform could have been carried out quickly and at a significantly lower cost and could have been able to cope much faster with the rapid and necessary changes that the platform will have to comply with. For these reasons, development in WordPress would be considered essential as this CMS platform guarantees a fast and developer-friendly environment. In this publication, as a contribution, we provide all the necessary information to develop a myDATA-like platform in a fast, economical and functional way using the WordPress CMS. Our contribution also contains the analysis of the minimum necessary amount of services of the myDATA platform in order to perform its basic functionalities, the description of the according database relational model, which must be implemented in order to provide the same functionality with the myDATA platform, and the analysis of available methods to quickly create the necessary forms and services. In addition, we study how to develop Artificial Intelligence mechanisms with a success rate reaching up to 90% for automatic tax violation detection algorithms.

Educational institutions face complex challenges when allocating limited teaching resources to specialized seminars, where budget, capacity, and balanced disciplinary representation must all be satisfied simultaneously. We address this for the first time in the educational domain by formulating the teacher seminar selection problem as a multi-dimensional knapsack variant with category-specific benefit multipliers. To solve it, we design a constraint-aware genetic algorithm that incorporates smart initialization, category-sensitive operators, adaptive penalties, and targeted repair mechanisms. In experiments on a realistic dataset representing multiple academic categories, our method achieved an 11.5% improvement in solution quality compared to the best constraint-aware greedy baseline while maintaining perfect constraint satisfaction (100% feasibility) vs. 0–30% for baseline methods. Statistical tests confirmed significant and practically meaningful advantages. For comprehensive benchmarking, we also implemented binary particle swarm optimization (PSO) and Tabu Search (TS) solvers with standard parameterizations. While PSO consistently produced feasible solutions with high budget utilization, its optimization quality was substantially lower than that of the GA. Notably, Tabu Search achieved the highest performance, with a mean fitness of 1557.3 compared to GA’s 1533.2, demonstrating that memory-based local search can be highly competitive for this problem structure. These findings show that metaheuristic approaches, particularly those integrating constraint-awareness into evolutionary or memory-based search, provide effective, scalable decision-support frameworks for complex, multi-constraint educational resource allocation.

Continuous customer activity generated by modern digital platforms drives the evolution of behavioral segments over time. Traditional customer segmentation methods typically rely on periodic batch analysis of historical data, producing static snapshots that may quickly become outdated and fail to capture emerging behavioral patterns. This paper presents a monitoring-oriented framework for detecting customer segment evolution and generating timely notifications about meaningful structural changes in the customer population. The proposed system continuously ingests user activity events, incrementally updates customer profiles, and periodically recomputes behavioral segments using fixed-k KMeans clustering over standardized recency, frequency, and monetary (RFM) features. To improve robustness and interpretability, the framework incorporates adaptive event scoring, stability-aware segment validation, drift-aware centroid matching, and persistence-based filtering of transient changes. These mechanisms reduce noisy alerts caused by repeated clustering updates while preserving meaningful signals about evolving customer behavior. The framework is evaluated on the Online Retail II and Instacart datasets under streaming simulation conditions. Experimental results show that the proposed approach maintains stable clustering structures, identifies persistent segment changes, and uncovers economically meaningful customer groups. Compared with static segmentation and periodic clustering baselines, the framework improves clustering quality while enabling continuous monitoring of segment evolution. Overall, the results suggest that adaptive monitoring can extend traditional customer segmentation into a practical continuous analytics process for moderate-scale dynamic environments.

Despite the significant volume of empirical research found in student-authored academic theses—particularly in the social sciences—these works are often poorly documented and difficult to discover within institutional repositories. A key reason for this is the lack of appropriate metadata frameworks that balance descriptive richness with usability. General standards such as Dublin Core are too simplistic to capture critical research details, while more robust models like the Data Documentation Initiative (DDI) are too complex for non-specialist users and not designed for use with student theses. This paper presents the design and validation of a lightweight, web-based metadata framework specifically tailored to document empirical research in academic theses. We are the first to adapt existing hybrid Dublin Core–DDI approaches specifically for thesis documentation, with a novel focus on cross-methodological research and non-expert usability. The model was developed through a structured analysis of actual student theses and refined to support intuitive, structured metadata entry without requiring technical expertise. The resulting system enhances the discoverability, classification, and reuse of empirical theses within institutional repositories, offering a scalable solution to elevate the visibility of the gray literature in higher education.