Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics is critical to proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This study underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. This paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates extract, transform, load (ETL) processes, artificial intelligence (AI) techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city’s coordinate-based problem mapping platform. Using an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios, allowing users to visualize, analyze, and predict the response of the urban system at the neighborhood level. This approach reveals antecedent and predictive patterns, trends, and correlations at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students’ retention services. Big Data mining and machine learning methods are applied to manipulate, analyze, and predict students’ failure, supporting self-directed learning. Despite the extensive application of data mining to education, the imbalance problem in minority classes of students’ attrition is often overlooked in conventional models. This document proposes a large data frame using the Hadoop ecosystem and the application of machine learning techniques to different datasets of an academic year at the Hellenic Open University. Datasets were divided into 35 weeks; 32 classifiers were created, compared and statistically analyzed to address the minority classes’ imbalance of student’s failure. The algorithms MetaCost-SMO and C4.5 provide the most accurate performance for each target class. Early predictions of timeframes determine a remarkable performance, while the importance of written assignments and specific quizzes is noticeable. The models’ performance in any week is exploited by developing a prediction tool for student attrition, contributing to timely and personalized intervention.

Smart cities, leveraging advanced data analytics, predictive models, and digital twin techniques, offer a transformative model for sustainable urban development. Predictive analytics plays a crucial role in proactive planning, enabling cities to adapt to evolving challenges. Concurrently, digital twin techniques provide a virtual replica of the urban environment, fostering real-time monitoring, simulation, and analysis of urban systems. This research underscores the significance of real-time monitoring, simulation, and analysis of urban systems to support test scenarios that identify bottlenecks and enhance smart city efficiency. The paper delves into the crucial roles of citizen report analytics, prediction, and digital twin technologies at the neighborhood level. The study integrates ETL/ELT processes, AI techniques, and a digital twin methodology to process and interpret urban data streams derived from citizen interactions with the city's coordinate-based problem mapping platform. By employing an interactive GeoDataFrame within the digital twin methodology, dynamic entities facilitate simulations based on various scenarios. This approach enables users to visualize, analyze, and predict the response of the urban system at the neighborhood level. Consequently, antecedent and predictive patterns, trends, and correlations are visualized at the physical level of each city area, leading to improvements in urban functionality, resilience, and resident quality of life.

In an epoch characterized by the swift pace of digitalization and urbanization, the essence of community well-being hinges on the efficacy of urban management. As cities burgeon and transform, the need for astute strategies to navigate the complexities of urban life becomes increasingly paramount. This study employs time series analysis to scrutinize citizen interactions with the coordinate-based problem mapping platform in the Municipality of Patras in Greece. The research explores the temporal dynamics of reported urban issues, with a specific focus on identifying recurring patterns through the lens of seasonality. The analysis, employing the seasonal decomposition technique, dissects time series data to expose trends in reported issues and areas of the city that might be obscured in raw big data. It accentuates a distinct seasonal pattern, with concentrations peaking during the summer months. The study extends its approach to forecasting, providing insights into the anticipated evolution of urban issues over time. Projections for the coming years show a consistent upward trend in both overall city issues and those reported in specific areas, with distinct seasonal variations. This comprehensive exploration of time series analysis and seasonality provides valuable insights for city stakeholders, enabling informed decision-making and predictions regarding future urban challenges.

Στο σημερινό ανταγωνιστικό επιχειρηματικό περιβάλλον, πολλές και διαφορετικές εφαρμογές παράγουν ένα τεράστιο όγκο δεδομένων, τα οποία συνήθως αναφέρονται ως μεγάλα δεδομένα. Τα συστήματα Επιχειρηματικής Ευφυίας και Ανάλυσης εφαρμόζονται όλο και περισσότερο από τον δημόσιο και τον ιδιωτικό τομέα για την παρακολούθηση σύνθετων δεδομένων σε Ταμπλό και την αποτελεσματική υποστήριξη λήψης αποφάσεων. Τα Επιχειρησιακά Ταμπλό, ως πλήρη συστήματα εκτέλεσης πληροφοριών, επιτρέπουν την SECTION A: theoretical papers, original research and scientific articles 102 εξέταση των επιχειρηματικών επιδόσεων προς καθορισμένους στόχους, σε ένα οπτικό διαδραστικό περιβάλλον. Τα ιδρύματα της εξ’ αποστάσεως εκπαίδευσης εφαρμόζουν παρόμοιες μεθοδολογίες για τη διαχείριση της έκρηξης εκπαιδευτικών δεδομένων χρησιμοποιώντας τεχνικές εξόρυξης δεδομένων και απεικόνισης. Η έλλειψη καθημερινής ανάλυσης δεδομένων σε πραγματικό χρόνο και η καθυστέρηση στην άμεση παρουσίαση κρίσιμων πληροφοριών, περιορίζουν τις πρόωρες αποφάσεις των εκπαιδευτικών. Με την εμφάνιση των Ταμπλό Ανάλυσης Εκπαιδευτικών Δεδομένων ως ένα πλήρες σύστημα μέτρησης, συλλογής, ανάλυσης και ταυτόχρονης απεικόνισης ζωτικών γνώσεων σε...

Attrition is one of the main concerns in distance learning due to the impact on the incomes and institutions reputation. Timely identification of students at risk has high practical value in effective students’ retention services. Big Data mining and machine learning methods are applied to manipulate, analyze and predict students’ failure, supporting self-directed learning. Despite the extensive application of data mining to education, the imbalance problem in minority classes of students’ attrition is often overlooked in conventional models. This document proposes a large data frame using the Hadoop ecosystem and the application of machine learning techniques to different datasets of an academic year at the Hellenic Open University. Datasets were divided into thirty-five weeks. Thirty-two classifiers were created, compared and statistical analyzed to address the minority classes’ imbalance of student’s failure. The algorithms metacost-SMO, and C4.5 provide the most accurate performance for each target class. Early predictions of timeframes determine a remarkable performance, while the importance of written assignments and specific quizzes is noticeable. The models’ performance in any week is exploited by developing a prediction tool for student attrition, contributing to timely and personalized intervention.