Financial institutions face many challenges in managing modern financial transactions and vast data volumes. To overcome these challenges, they are increasingly harnessing advanced data man- agement technologies such as artificial intelligence and cloud computing. This paper presents a com- prehensive review of how these tools transform financial decision-making in various domains and ap- plications. We analyzed both foundational and recent advancements using a rigorous methodology based on the PRISMA 2020 guideline. Our findings indicate that many major financial institutions are adopting AI-driven solutions to potentially enhance real-time risk assessment, transactional efficiency, and predictive analytics. While they bring benefits like faster decision-making and reduced operational costs, they also pose challenges like data security and integration complexities that require further research and development. Looking ahead, we envision a more integrated, responsive, and secure financial ecosystem that leverages the convergence of AI, cloud computing, and advanced data storage. This synthesis underscores the significance of contemporary data management solutions in shaping the future of data-driven financial services, offering a guideline for stakeholders in this evolving domain.

Modern financial institutions now manage increasingly advanced data-related activities and place a growing emphasis on environmental and energy impacts. In financial modeling, relational databases, big data systems, and the cloud are integrated, taking into consideration resource optimization and sustainable computing. We suggest a four-layer architecture to address financial data processing issues. The layers of our design are for data sources, data integration, processing, and storage. Data ingestion processes market feeds, transaction records, and customer data. Real-time data are captured by Kafka and transformed by Extract-Transform-Load (ETL) pipelines. The processing layer is composed of Apache Spark for real-time data analysis, Hadoop for batch processing, and an Machine Learning (ML) infrastructure that supports predictive modeling. In order to optimize access patterns, the storage layer includes various data layer components. The test results indicate that the processing of market data in real-time, compliance reporting, risk evaluations, and customer analyses can be conducted in fulfillment of environmental sustainability goals. The metrics from the test deployment support the implementation strategies and technical specifications of the architectural components. We also looked at integration models and data flow improvements, with applications in finance. This study aims to enhance enterprise data architecture in the financial context and includes guidance on modernizing data infrastructure.

A smart city implies a consistent use of technology for the benefit of the community. As the city develops over time, components and subsystems such as smart grids, smart water management, smart traffic and transportation systems, smart waste management systems, smart security systems, or e-governance are added. These components ingest and generate a multitude of structured, semi-structured or unstructured data that may be processed using a variety of algorithms in batches, micro batches or in real-time. The ICT architecture must be able to handle the increased storage and processing needs. When vertical scaling is no longer a viable solution, Hadoop can offer efficient linear horizontal scaling, solving storage, processing, and data analyses problems in many ways. This enables architects and developers to choose a stack according to their needs and skill-levels. In this paper, we propose a Hadoop-based architectural stack that can provide the ICT backbone for efficiently managing a smart city. On the one hand, Hadoop, together with Spark and the plethora of NoSQL databases and accompanying Apache projects, is a mature ecosystem. This is one of the reasons why it is an attractive option for a Smart City architecture. On the other hand, it is also very dynamic; things can change very quickly, and many new frameworks, products and options continue to emerge as others decline. To construct an optimized, modern architecture, we discuss and compare various products and engines based on a process that takes into consideration how the products perform and scale, as well as the reusability of the code, innovations, features, and support and interest in online communities.

In this study, we explore the challenges and potential solutions to blockchain-based voting. As a first step, we present a comparison of the relevant platforms for implementing smart contracts in decentralized applications (dApps). We analyze the top platforms, highlighting their advantages and disadvantages, their architecture, and which are more reliable for developing smart contracts. The goal is to find a technology that offers various facilities to the developer and multiple functionalities and performance in the development of smart contracts in a field that has seen an incredible pace of innovation. Based on the findings from our research, we propose a framework based on blockchain technology and smart contracts for university-level voting based on blockchains.

This study explores the evolution of wine regulations, trade, and production, with a particular focus on the European Union countries and associated countries. The primary objective is to analyze how historical, regulatory, and economic factors have influenced the wine industry from antiquity to modern times. We assess the administrative role of wine in Roman society, its governance in medieval Europe, and the formation of contemporary regulatory frameworks, such as the EU’s Protected Designation of Origin (PDO) system. The study employs a systematic review approach inspired by the PRISMA 2020 statement, incorporating a diverse array of academic journals, historical texts, and statistical data. We use k-means clustering and Principal Component Analysis (PCA) to analyze 2020 production data and value comparisons among European wine varieties. This quantitative analysis reveals patterns in production and trade profiles across European countries, highlighting Romania’s unique position in the non-PDO segment and its strategic balance of wine production and value. The paper also examines the impact of climate change on viticulture and the industry’s response to sustainability challenges. By integrating historical insights with contemporary data, our research provides a comprehensive view of the wine industry’s evolution, emphasizing the interplay of government oversight, market dynamics, and environmental considerations in shaping the European wine landscape.