Diana Andreea Căuniac’s research while affiliated with Bucharest University of Economic Studies and other places

As vast amounts of data are generated from various sources such as social media, sensors and online transactions, the analysis of Big Data offers organizations the ability to derive insights and make informed decisions. Simultaneously, IoT connects physical devices, enabling real-time data collection and exchange that transforms interactions within smart homes, cities and industries. The intersection of these fields is essential, leading to innovations such as predictive maintenance, real-time traffic management and personalized solutions. Utilizing a dataset of 8159 publications sourced from the Web of Science database, our research employs Natural Language Processing (NLP) techniques and selective human validation to analyze abstracts, titles, keywords and other useful information, uncovering key themes and trends in both Big Data and IoT research. Six topics are extracted using Latent Dirichlet Allocation. In Topic 1, words like “system” and “energy” are among the most frequent, signaling that Topic 1 revolves around data systems and IoT technologies, likely in the context of smart systems and energy-related applications. Topic 2 focuses on the application of technologies, as indicated by terms such as “technologies”, “industry” and “research”. It deals with how IoT and related technologies are transforming various industries. Topic 3 emphasizes terms like learning and research, indicating a focus on machine learning and IoT applications. It is oriented toward research involving new methods and models in the IoT domain related to learning algorithms. Topic 4 highlights terms such as smart, suggesting a focus on smart technologies and systems. Topic 5 touches upon the role of digital chains and supply systems, suggesting an industrial focus on digital transformation. Topic 6 focuses on technical aspects such as modeling, system performance and prediction algorithms. It delves into the efficiency of IoT networks with terms like “accuracy”, “power” and “performance” standing out. https://www.mdpi.com/1424-8220/25/3/906#

Monitoring the performance of a database is the act of measuring the performance of a real-time database in order to determine problems and other factors that can cause problems in the future. It is also a good way to determine which areas of the database can be improved or optimized in order to increase efficiency and performance. This is usually done by software and monitoring tools, either incorporated into the database management software or installed from third-party suppliers. The main objective of database performance monitoring is to evaluate the performance of a database server, for both hardware and software. This involves taking snapshots of performance over time to determine the exact moment that problems such as crashes occur, so you can understand exactly what caused those problems at that exact time and hopefully find a proper solution. Through this paper, you will find a short introduction regarding the true meaning of database performance and the aspects that need the most attention when making an analysis. You will also find a comparison between the performance of two databases and what could be improved.

Air pollution affects humans, animals, plants and the environment altogether, making this a topic of great importance. Air quality is directly linked to everyone’s health and overall wellbeing. European Governments started to monitor air pollution and make the extracted data openly available. With this and with the use of a free API from World Air Quality Index portal (waqi.info), we developed the solution euAirQuality. euAirQuality’s aim is to extract real-time pollution data, visualize it both as an interactive world map and as station-centered historical data, and analyze the extracted data. It also messages users when there is a forecasted AQI value that exceeds a user set threshold. euAirQuality comes with the opportunity to let end-users do their own air quality analysis, by selecting input and output variables for the model, while also choosing the time frame and location for the analysis. RMSE values are computed for six different models, ARMA, ARIMA, SARIMA, ANN, MLR, and simple Decision Tree (DT) in order to see which model performs best. This feature helps analysts to compare the models to see which is more suited for different stations, pollutants, or combinations between them. We also performed a simulation for the main station in Bucharest. When modelling both PM10 and NO2, time series models outperformed the others. ARMA models turned out to be the best.