Big Data and Cloud Computing as two mainstream technologies, are at the center of concern in the IT field. Every day a huge amount of data is produced from different sources. This data is so big in size that traditional processing tools are unable to deal with them. Besides being big, this data moves fast and has a lot of variety. Big Data is a concept that deals with storing, processing and analyzing large amounts of data. Cloud computing on the other hand is about offering the infrastructure to enable such processes in a cost-effective and efficient manner. Many sectors, including among others businesses (small or large), healthcare, education, etc. are trying to leverage the power of Big Data. In healthcare, for example, Big Data is being used to reduce costs of treatment, predict outbreaks of pandemics, prevent diseases etc. This paper, presents an overview of Big Data Analytics as a crucial process in many fields and sectors. We start by a brief introduction to the concept of Big Data, the amount of data that is generated on a daily bases, features and characteristics of Big Data. We then delve into Big Data Analytics were we discuss issues such as analytics cycle, analytics benefits and the movement from ETL to ELT paradigm as a result of Big Data analytics in Cloud. As a case study we analyze Google’s BigQuery which is a fully-managed, serverless data warehouse that enables scalable analysis over petabytes of data. As a Platform as a Service (PaaS) supports querying using ANSI SQL. We use the tool to perform different experiments such as average read, average compute, average write, on different sizes of datasets.

The integration of the Internet of Things (IoT) in education has fundamentally transformed personalized online learning (POL), enabling adaptive, context-aware environments that tailor learning based on real-time environmental data or device usage. This approach fosters learner-centered environments that customize content and pace according to individual needs and preferences. All are powered by interconnected devices and data analysis. This review systematically explores various technological and pedagogical approaches to integrate IoT in POL systems, including devices, communication technologies, context-aware mechanisms, integration frameworks, computing technologies, and implemented pedagogical methods. By examining 61 peer-reviewed studies, this research categorizes IoT technological and pedagogical approaches, evaluates their impacts on personalization, and identifies challenges such as data privacy and resource constraints. The key findings explore how IoT-enhanced POL significantly improves learner engagement, adaptive learning, and academic outcomes, assessed via performance analytics, tests, and questionnaires. However, gaps in interoperability standards, learner profiling, cognitive load modeling, and the ethical use of data highlight the need for a unified framework. In addition, it emphasizes the transformative potential of IoT in creating personalized and inclusive learning environments. The study concludes by proposing an IoT-based POL framework and recommends directions for future research and practical applications.

This article proposes a new concept of microservice-based architecture for the future of distributed systems. This architecture is a bridge between Internet-of-Things (IoT) devices and applications that are used to monitor cattle health in real time for the physical and health parameters of cattle, where microservice architecture is introduced that enables this form of monitoring. Within this architecture, machine-learning algorithms were used to predict cattle health and inform farmers about the health of each cattle in real time. Within this architecture, six microservices were proposed that had the tasks of receiving, processing, and sending data upon request. In addition, within the six microservices, a microservice was developed for the prediction of cattle health using algorithms from machine learning using the LightGBM algorithm. Through this algorithm, it is possible to determine the percentage value of the health of each head of cattle in the moment, based on the parameters that are sent from the mobile node. If health problems are identified in the cattle, the architecture notifies the farmer in real time about the problems that the cattle have. Based on the proposed solution, farmers will have 24 h online access to monitor the following parameters for each head of cattle: body temperature, heart rate, humidity, and position.

Anonymity and privacy in the electoral process are mandatory features found in any democratic society, and many authors consider these fundamental civil liberties and rights. During the election process, every voter must be identified as eligible, but after casting a vote, the voter must stay anonymous, assuring voter and vote unlinkability. Voter anonymity and privacy are the most critical issues and challenges of almost all electronic voting systems. However, vote immutability must be assured as well, which is a problem in many new democracies, and Blockchain as a distributed technology meets this data immutability requirement. Our paper analyzes current solutions in Blockchain and proposes a new approach through the combination of two different Blockchains to achieve privacy and anonymity. The first Blockchain will be used for key management, while the second will store anonymous votes. The encrypted vote is salted with a nonce, hashed, and finally digitally signed with the voter’s private key, and by mixing the timestamp of votes and shuffling the order of cast votes, the chances of linking the vote to the voter will be reduced. Adopting this approach with Blockchain technology will significantly transform the current voting process by guaranteeing anonymity and privacy.

This study analyzed the student dropout rate at the Faculty of Electrical and Computer Engineering (FECE) of the University of Pristina, Kosovo, from 2001 to 2015. This phenomenon is a concern for many countries, not only for students due to its emotional effects and further consequences for their university careers but also for institutions and society in general due to its broader negative impact on their authority. Over the past two decades, a significant number of students have been enrolled in these programs. However, we identified several factors that influenced young people to abandon their studies. The high dropout rate associated with FECE programs results in a lack of professionals in these areas and negatively affects the country’s economy. The main reasons given by students for such dropout were unrelated to whether students were from urban or rural areas or economic factors but were strongly related to high school programs and, to a somewhat lesser degree, to poor academic performance in certain courses. This research suggests opportunities for reducing dropout rates, as improvement in this context can be achieved by updating curricula, employing contemporary literature, applying modern teaching methodologies, improving laboratories, and reforming the behavior of certain professors. The main purpose of this study is to find a compromise between the large number of students enrolled in FECE and dropout rates; however, the findings highlight a phenomenon that is very complex and multidimensional, especially when dropout occurs after the first year of study.

Efficient and scalable simultaneous interaction between many web servers requires special handling of such interaction. Google Datastore provides highly available and reliable distributed storage and access on different types of data. With the rapid growth of distributed systems, efficient data provide is of critical importance. In this paper we present the possibilities which Google Application Engine and cloud computing provides to build distributed applications by specifically discussing the options to store the data on the cloud in a reliable way. Important is identification of the programming languages and tools which are suitable for designing of distributed systems using the Google possibilities as a framework for developing such systems. The design of distributed systems by using Google features together with the libraries in Java and Python programming language are examined in this paper.