Data Management Solutions Research Articles

FUTURE TRENDS IN SQL DATABASES AND BIG DATA ANALYTICS: IMPACT OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE

This study systematically reviews the integration of machine learning (ML) and artificial intelligence (AI) into SQL databases and big data analytics, highlighting significant advancements and emerging trends. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive review of 60 selected articles published between 2010 and 2023 was conducted. The findings reveal substantial improvements in query optimization through ML algorithms, which adapt dynamically to changing data patterns, reducing processing times and enhancing performance. Additionally, embedding ML models within SQL databases facilitates real-time predictive analytics, streamlining workflows, and improving the accuracy and speed of predictions. AI-driven security systems provide proactive and real-time threat detection, significantly enhancing data protection. The development of hybrid systems that combine relational and non-relational databases offers versatile and efficient data management solutions, addressing the limitations of traditional systems. This study confirms the evolving role of AI and ML in transforming data management practices and aligns with and extends previous research findings.

Secure environmental, social, and governance (ESG) data management for construction projects using blockchain

Environmental, social, and governance (ESG) considerations are increasingly becoming imperative and obligatory across various industries. The ESG performance within the architecture, engineering, and construction (AEC) industry is under heightened market scrutiny. However, current ESG management in construction is still in its infancy due to two limitations: (1) a deficiency in ESG knowledge, such as indicators pertinent to construction activities, and (2) a lack of data security in ESG management, culminating in inefficient and unreliable environmental management practices. Therefore, this paper employs the Design Science Research Method (DSRM) to introduce a Blockchain-ESG Integrated (BESGI) framework, facilitating traceable ESG data management within construction projects. This framework presents three significant contributions. First, it identifies ten AEC-ESG indicators by analyzing ESG methods. Second, it proposes a mapping approach for AEC-ESG indicators to construction projects for key ESG information access and data source identification. Third, it develops a blockchain-based data management mechanism for traceable ESG data management in the BESGI framework. It validates and evaluates the framework in a construction project in Hong Kong. The results show that the framework is usable and can save labor costs by 20.15 % compared to traditional ESG management. This study offers a secure data management solution for ESG analysis of construction projects.

Barriers to Effective Electronic Records Management in Public Sector Organizations in East Africa

Purpose: The aim of the study was to investigate the barriers to effective electronic records management in public sector organizations in East Africa. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Barriers to effective electronic records management (ERM) in East African public sector organizations include inadequate technology infrastructure, limited financial resources, staff training gaps, policy implementation challenges, resistance to change from paper-based systems, data security concerns, and insufficient organizational commitment. Addressing these barriers requires holistic strategies encompassing technological upgrades, enhanced financial support, robust training programs, streamlined policies, and fostering a culture supportive of ERM practices. Unique Contribution to Theory, Practice and Policy: Institutional theory, technology acceptance model (TAM) & resource dependency theory may be used to anchor future studies on barriers to effective electronic records management in public sector organizations in East Africa. Implementing cloud-based solutions for data storage and management can improve accessibility and data integrity, overcoming technological barriers to effective ERM implementation. Depoliticize ERM processes by establishing bipartisan support and clear governance frameworks. Policy reforms should include guidelines for managing political transitions without disrupting ERM continuity

Integrating FAIR Experimental Metadata for Multi-omics Data Analysis

The technological advancements in bio-science research are resulting in the generation of vast amounts of complex and heterogeneous data sets from individual studies. Efficient Research Data Management solutions based on FAIR principles can guide the research groups toward standardizing and packaging the study-specific research results into uniquely identifiable digital objects that are easily traceable and identifiable, leading to knowledge discovery, collaboration, and innovation. However, to explore the inter-dependencies among data sets originating from different study disciplines, it is crucial to deploy a generic data-centric RDM solution that overcomes inherent challenges and helps manage complex data sets. This solution should respect participating groups’ data and security policies while providing an integrated data view. In this paper, we introduce ARC Registry, a cloud-native search & exploration application that integrates experimental metadata from individual research groups that have deployed PLANTdataHUB (FAIR-RDM) solution. The focus is on the parts of PLANTdataHUB and ARC Registry that facilitate near-real-time integration of experimental metadata from standardized digital objects (Annotated Research Contexts) across participating research groups.

Towards Municipal Data Utilities: Experiences Regarding the Development of a Municipal Data Utility for Intra- and Intermunicipal Actors within the German City of Mainz

This paper describes the requirements analysis phase towards the establishment and implementation of a municipal data utility (KDW = Kommunales Datenwerk, German) to facilitate data sharing between intra- and intermunicipal stakeholders. Against the backdrop of increasing digitisation and the growing importance of data-driven decision making in municipal governance, this paper aims to address the pressing need for efficient data management solutions within and across municipalities. Based on a structured self-developed methodology, the authors use a qualitative research approach: the paper examines the experiences and challenges encountered during the requirements phase, the design phase, and the development phase of the KDW. The findings indicate that the establishment of a robust KDW requires (1) extensive stakeholder engagement, (2) clear governance structures, and (3) a robust technical infrastructure. In addition, the study highlights the critical importance of establishing a sound legal framework that addresses data ownership, privacy, security and regulatory compliance. Addressing legal and regulatory barriers to data sharing is paramount to the successful implementation and operation of the KDW. The paper concludes by highlighting the potential benefits of KDWs and outlining future work. The overall methodology, approach, and outcome are validated within the city of Mainz, and the lessons learned are accommodated in the insights presented in the rest of the paper.

A Review on Blockchain Technology

Blockchain technology has emerged as a disruptive force with the potential to revolutionize various industries by offering decentralized, secure, and transparent solutions for data management and transactions. Initially conceptualized as the underlying technology for cryptocurrencies like Bitcoin, blockchain has evolved into a versatile tool applicable across diverse domains such as finance, supply chain management, healthcare, and government services. This paper provides an overview of blockchain technology, its fundamental principles, and key components. It explores the decentralized consensus mechanisms, cryptographic techniques, and data structures that underpin blockchain networks, ensuring immutability, transparency, and integrity of recorded transactions. Furthermore, the paper examines the challenges and opportunities associated with blockchain adoption, including scalability, interoperability, regulatory concerns, and environmental impact. Through a comprehensive review of existing literature and case studies, this research elucidates the potential benefits of blockchain technology, such as increased efficiency, reduced costs, enhanced security, and improved trust among participants. Additionally, it highlights ongoing developments, innovations, and future directions in blockchain research and implementation, emphasizing the need for interdisciplinary collaboration and regulatory frameworks to realize the full potential of this transformative technology. Keywords : IOT, blockchain, cryptocurrency, smart contracts, consensus algorithms, internet of things.

Streamlining Toll Management: A Survey of Business Requirement Solutions

Effective toll control performs a pivotal role in improving traffic flow, decreasing congestion, and enhancing overall transportation performance. However, toll plaza operators frequently face demanding situations in efficaciously handling statistics and monitoring transactional activities. This studies paper proposes a secure, fast, and dependable statistics control answer tailor-made to the precise wishes of toll plaza operators. By implementing this solution, toll plaza proprietors can successfully track income and loss states, examine regular transaction statistics, and optimize traffic control measures. Through the improvement and implementation of this solution, massive enhancements in traffic flow efficiency and congestion reduction may be achieved, ultimately leading to more advantageous transportation systems. This paper offers a practical method to addressing the complexities of toll control, imparting insights into the advantages and impacts of adopting such answers withinside the transportation sector. Keywords-Toll management, Traffic flow, Congestion reduction, Transportation efficiency, Data management solution, Profit tracking, Transaction analysis, Traffic control, Toll plaza operators, Transportation systems

The intelligent prediction and assessment of financial information risk in the cloud computing model

cloud computing (cloud computing) is a kind of distributed computing, referring to the network "cloud" will be a huge data calculation and processing program into countless small programs, and then, through the system composed of multiple servers to process and analyze these small programs to get the results and return to the user. This report explores the intersection of cloud computing and financial information processing, identifying risks and challenges faced by financial institutions in adopting cloud technology. It discusses the need for intelligent solutions to enhance data processing efficiency and accuracy while addressing security and privacy concerns. Drawing on regulatory frameworks, the report proposes policy recommendations to mitigate concentration risks associated with cloud computing in the financial industry. By combining intelligent forecasting and evaluation technologies with cloud computing models, the study aims to provide effective solutions for financial data processing and management, facilitating the industry's transition towards digital transformation.

Smart contract: Revolutionizing transactions in the digital age

Blockchain technology is a revolutionary concept that has transformed various industries, offering decentralized and secure solutions for data management. At its core, a blockchain is a distributed and immutable ledger that records transactions across a network of computers. One of the key features of blockchain is its transparency, as each participant in the network has access to the same information. The application of blockchain technology extends across diverse sectors, with finance being the most promising and well-known application domain. Cryptocurrencies, like Bitcoin and Ethereum, rely on blockchain to facilitate secure and transparent transactions. The decentralized nature of blockchain eliminates the need for intermediaries, reducing transaction costs and increasing efficiency. Beyond finance, blockchain is making significant strides in supply chain management. The emergence of blockchain technology has paved the way for a transformative innovation in the realm of digital transactions: smart contracts. These self-executing contracts encoded in blockchain have garnered significant attention for their potential to revolutionize the way agreements are made, executed, and enforced. This paper explores the concept of smart contracts, delving into the underlying technology, finding applications across various industries, legal implications, and future prospects.

Distributed Systems for Data-Intensive Computing in Cloud Environments: A Review of Big Data Analytics and Data Management

Because of the increasing increase of data, which is frequently referred to as "big data," many different businesses have been severely impacted in recent years, necessitating the implementation of sophisticated data management and analytics solutions. By virtue of the fact that it provides scalable resources for applications that are data-intensive, cloud computing has emerged as an indispensable platform for the management of these enormous databases. The evolving landscape of distributed systems in cloud settings is the primary emphasis of this study, which is situated within the framework of big data analytics and data management. With the purpose of providing a comprehensive overview of distributed systems that are used in cloud settings for data-intensive computing, the review article seeks to offer. Furthermore, it evaluates the many ideas, techniques, and technical improvements that have been established in order to properly manage, store, and analyse large amounts of data. A comprehensive literature evaluation of recently published scientific references was successfully completed by our team. The analysis takes into account the theoretical foundations, as well as the research that has already been conducted on distributed computing systems, cloud-based data management, and enormous data analytics. The study places an emphasis on the significant role that distributed computing plays in ensuring the success of big data analytics. The interplay between distributed systems and cloud computing paradigms has resulted in the development of solutions that are robust, scalable, and economical for activities that need a significant amount of data. It is still a huge problem to ensure that data security, privacy, and interoperability are maintained across the many cloud services.

Building a Full Stack Solution for Seamless Device Connectivity and Data Management

This paper involves developing a full-stack IoT application with a cloud server. The project encompasses defining the project scope, selecting suitable hardware, developing firmware/software for IoT devices, setting up a cloud server with IoT services, configuring data ingestion and storage, building a backend application for data processing, creating a frontend UI/dashboard, establishing bidirectional communication, conducting testing, deploying the applications, and implementing continuous monitoring and optimization. By following these steps, the project aims to create a comprehensive IoT solution that collects, analyzes, and visualizes data from connected devices, enabling remote monitoring and control.

A lab-centric, workflow-based data management system for environmental DNA research

The adoption of environmental DNA approaches as a standard tool for biodiversity monitoring leads to the increase in the number of eDNA-based species occurrence records; however, considerable disparity remains in the nature and quality of associated information, much of it unpublished and/or poorly parametrised. A robust system for tracking biological materials from their point of origin through laboratory analyses is required to connect inferred taxon occurrences with analytical history and provenance data. The bulk of eDNA research is currently driven by small-scale operations where the tasks of digitisation, organisation and cross-referencing field records with laboratory analytical data and biomaterial sample location, are often performed manually and disconnected. We present an integrative, full-stack data management solution that provides a structured ontological concept, a minimalist data schema for eDNA research and a software application prototype designed to facilitate real-time digitisation, parsing, annotation and archival of eDNA data. The system tracks the provenance and analytical history of biological samples through a structured hierarchy of events, linked with associated digital file attachment archives, such as images and raw sequence files, and with inferred taxonomic occurrence records. The data entry process is compartmentalised and incorporated into the corresponding stages of standard operations used in fieldwork, biological collection management and laboratory analysis. Resulting data records can be integrated into various output formats required for large-scale analytics, publication and/or submission to global data aggregators. The prototype is implemented on the Microsoft 365 platform as a relational database (Access) linked to cloud-based data tables (SharePoint) and a set of associated data conversion spreadsheets (Excel). The system is designed primarily around the data management needs of small research labs; however, it is scalable to larger institutions and inter-institutional academic networks.

Review of the state of practice in geovisualization in the geosciences

Geosciences modelling and 3D geovisualization is growing and evolving rapidly. Driven by commercial urgency and an increase in data from sensor-based sources, there is an abundance of opportunities to analyze geosciences data in 3D and 4D. Geosciences modelling is developing in GIS based systems, 3D modelling through both game engines and custom programs, and the use of extended reality to further interact with data. The key limitations that are currently prevalent in 3D geovisualization in the geosciences are GIS representations having difficulty displaying 3D data and undergoing translations to pseudo-3D, thus losing fidelity, financial and personnel capital, processing issues with the terabytes worth of data and limited computing, digital occlusion and spatial interpretation challenges with users, and matching and alignment of 3D points. The future of 3D geovisualization lies in its accelerated growth, data management solutions, further interactivity in applications, and more information regarding the benefits and best practices in the field.

A Network Modeling Systematics for Transition Paths Toward Climate Neutral Gas Networks—NeMoSys ['nεmɒsis] –

Meaningful network modeling requires high spatial and temporal resolution and thus adds further complexity to the already complex energy systems analyses; unsecured and varying framework conditions, as well as their changes over time, have to be taken into consideration on the backdrop of path‐dependencies for network development over time. This perspectives article provides a systematic framework for gas network modeling, starting with a morphology of modeling dimensions and elements and the hierarchy of influencing factors. It gives an overview on the necessary data and attributes, development steps, and assessment criteria; it showcases solutions for data management, fluid dynamic network simulation, and results assessment that have been applied and developed in the course of the TransHyDE project and other related research projects. Framework and solutions are merged into a consolidated network modeling systematics that shall be further developed and made available to researchers as well as to network planners for practical application and governmental bodies for assessments. The systematics presented here will be further developed, applied, and operationalized in a database and tool compilation with open‐access for collaboration and further development.

ReUseData: an R/Bioconductor tool for reusable and reproducible genomic data management

BackgroundThe increasing volume and complexity of genomic data pose significant challenges for effective data management and reuse. Public genomic data often undergo similar preprocessing across projects, leading to redundant or inconsistent datasets and inefficient use of computing resources. This is especially pertinent for bioinformaticians engaged in multiple projects. Tools have been created to address challenges in managing and accessing curated genomic datasets, however, the practical utility of such tools becomes especially beneficial for users who seek to work with specific types of data or are technically inclined toward a particular programming language. Currently, there exists a gap in the availability of an R-specific solution for efficient data management and versatile data reuse.ResultsHere we present ReUseData, an R software tool that overcomes some of the limitations of existing solutions and provides a versatile and reproducible approach to effective data management within R. ReUseData facilitates the transformation of ad hoc scripts for data preprocessing into Common Workflow Language (CWL)-based data recipes, allowing for the reproducible generation of curated data files in their generic formats. The data recipes are standardized and self-contained, enabling them to be easily portable and reproducible across various computing platforms. ReUseData also streamlines the reuse of curated data files and their integration into downstream analysis tools and workflows with different frameworks.ConclusionsReUseData provides a reliable and reproducible approach for genomic data management within the R environment to enhance the accessibility and reusability of genomic data. The package is available at Bioconductor (https://bioconductor.org/packages/ReUseData/) with additional information on the project website (https://rcwl.org/dataRecipes/).

Digitalization of Mandatory Periodic Revision of Switches by the Railway Infrastructure Manager

The issue of diagnosing and maintaining railway infrastructure is an essential part of its operation. This issue also encompasses the topic of switch inspections. Switches are one of the most heavily used components in a railway track, which is why their inspections are necessary at regular, relatively short intervals. To ensure the safety and smooth operation of the railway, switch inspections are carried out at quarterly intervals. Currently, the results of switch inspections in the conditions of the Czech and Slovak railway infrastructure managers are processed manually, which is a limiting factor in maintenance at present. The current state of scientific and technical knowledge creates opportunities for improving and streamlining measurements on railway infrastructure, which can result in increased safety and reliability of railway operations. This article presents partial results of a research project focused on the possibilities of introducing digital processing of measurement results from quarterly switch inspections in the conditions of the Czech infrastructure manager - Správa železnic, n. o., and the Slovak infrastructure manager - Železnice Slovenskej republiky. In brief, it highlights the advantages of measuring the structural elements of switches with a digital "gauge" compared to manual methods. The second part of the article outlines the solution for digital data management and possibilities for their evaluation.

Towards data storage scheme in blockchain based serverless environment: AES encryption and decryption algorithm approach

In today's digital age, data storage has become an increasingly important issue. The exponential growth of data has led to the need for secure and private storage solutions. Unfortunately, without a standardized protocol, the security and privacy of sensitive data can be a challenge. However, blockchain technology has emerged as a promising solution for secure data storage. Blockchain's decentralized and immutable nature provides a comprehensive solution for the security and privacy of all types of data. In this research, we propose an innovative framework that leverages the benefits of blockchain technology to securely handle and store data in a serverless environment of distributed nodes. To ensure the highest level of security, we evaluated four encryption algorithms - Blowfish, RC4, DES, and AES - for storing data in a permissioned blockchain network. We found that AES encryption and decryption algorithms provide the best solution for creating a decentralized, immutable coordinate system. Our proposed framework is based on a permissioned blockchain network that enables multiple users to join the network through suitable identity verification processes, and each user is assigned certain special and designated permissions to perform actions. This framework provides a secure and efficient solution for the storage of all types of data, ensuring privacy and security. Our proposed framework offers an innovative solution for secure data storage and management in a decentralized environment. This research has practical implications for organizations that need to store sensitive data securely, and it also contributes to the ongoing development of blockchain technology.

Revolutionizing Data Management through Cloud-Based Data Fusion Platforms in Distributed Network Architectures

Data management is developing rapidly, and we need solutions that can handle massive volumes of diverse data. Especially for cloud-based data fusion and global network designs. Our research offers a fresh solution. Each difficult formula in this manner improves the system. Standardizing, matching, translating, and merging data from several sources is the fundamental strategy for data integration and management. We found that this alternative is superior to standard data management systems for growing, working fast, consistently, securely, and accurately integrating data, as well as cost-effectiveness. Data's visual presentation enhances the method's advantages and shows its potential. This research proves the technique works and illustrates how it may be utilized to advance the field. Supporting today's sophisticated data systems is a major advance. It's a solid, scalable data management solution that can evolve.

Zero Trust-Based Blockchain Based IoT Security with Consensus and Access Control Framework

As the Internet and computer technology develop, more gadgets are linked wirelessly, expanding the Internet of Things (IoT). IoT is a huge network of sensors and gateways that links them. IoT devices generate images, music, video, digital signals, and more by interacting with their surroundings. To exchange resources and information, all IoT equipment and apps may connect to the Internet. Everything is connected in our world. Due to the broad deployment and massive size of IoT devices, access control of device resources is problematic. Obtaining IoT device resources unlawfully will have major implications since they include personal and sensitive information. Many systems and situations employ access control technologies to secure resources. Discriminatory, identity-based, and MAC access control schemes are traditional (mandatory access control). However, these centralized methods have single-point failure, scalability issues, poor dependability, and low throughput. IoT devices may belong to several organizations or people, be mobile, and function badly, making centralized access management problematic. Another innovative data management solution is blockchain, which uses distributed storage to stabilize data. A transaction writes the data reading or modification record into a block, and the blocks are connected as a chain using a hash to maintain data integrity. It synchronizes data between nodes via a peer-to-peer network and consensus process, assuring data consistency for blockchain network participants. Zero Trust-Based Blockchain, an open source blockchain development platform, offers more efficient consensus methods, larger throughputs, smart contracts, and support for different organizations and ledgers. Proposed work build the fabric-IoT access control system using Zero Trust-Based Blockchain to apply blockchain technology to IoT access control in this study. Distributed processing and storage for IoT data may solve these critical issues with blockchain. Thus, developing distributed IoT-based e-healthcare services using blockchain technology may have been feasible. FabricIoT can keep records, handle dynamic access control, and solve the IoT access control problem using distributed architecture.

IoT 엣지디바이스를 이용한 경량화 클라우드 컴퓨팅환경 구축 및 검증

This study validated a cloud computing environment using edge devices for Internet of Things (IoT) sensor data collection and communication. Lightweight cloud infrastructure enhanced resource efficiency, minimized latency, and optimized data storage and processing. Technical achievements encompass standardized data exchange, improved device scalability and flexibility through software technologies, real-time data updates via synchronization, and the creation of an API-based user interface for an intuitive dashboard. The study substantiates the efficient establishment and validation of a cloud computing environment using edge devices, providing practical solutions for IoT data management and processing.