<strong>Purpose: </strong><em>With the emergence of Online Purchasing, Product Identification is an essential model that allows the seller to add a product to a decentralized platform such as Blockchain and allows buyers to purchase the product from the decentralized platform. Fraud products, counterfeiting, and duplication are the current marketplace's major problems. This aims to develop a system for verifying product identification with their information, ownership, and validity detail.</em> <strong>Design/Methodology/Approach: </strong><em>The proposed system applies Extreme Programming (XP) to reduce the risk caused by the fixed-time project using new technology and thus the final project could be delivered in time. Solidity and metamask being new technologies were unstable and to adopt the changes, the agile development model was the best through ABI and the bytecode are deployed into the Ethereum Blockchain.</em> <strong>Findings/Result: </strong><em>This system maintains the buyers, sellers, and product details in a decentralized blockchain platform. This research details the entire product development process from planning, analysis, design, implementation, and testing for systematic online purchasing. Verifying the product ownership and its information to get the original product is the major difficulty in this space, but this research systematically solves some of those problems. This signifies an improvement in the current centralized way of purchasing goods online, where the information remains as it is entered by the seller while listing the product in Nepal and developing countries context.</em> <strong>Originality/Value: </strong><em>The study has produced a decentralized, reliable, secure, and third-party independent marketplace for buying and selling products for fraud free market.</em> <strong>Paper Type: </strong><em>Research paper</em>
Blockchain technology has seen practical application since 2009. However, the suitable selection of blockchain technology for any particular application is currently completed using no standard formalised process with limited understanding of where a particular blockchain is in its development maturity against its peers, or peer sector. Technology Readiness Levels (TRLs) offer a common and well known method for identifying the maturity of a technology. However, new TRL definitions have been required to address sector specific requirements. This work addresses these challenges and presents a novel Blockchain Readiness Level (BRL) for identifying the maturity of blockchain technology.
The analysis of the causes or drivers of the adoption of big data analytics and blockchain and their subsequent influence on firm performance has become a significant need as a direct result of the rapidly expanding popularity of business intelligence. The purpose of this research is to present a model that investigates the direct and indirect influence of business intelligence on firm performance through the mediating roles of the adoption of big data analytics and blockchain. The analysis is based on data collected from a representative sample of 387 employees from 12 Information technology (IT) firms operating in Croatia. The study investigates these connections using a structural equation modeling. The findings showed that business intelligence has a direct and significant influence on firm performance. In addition, business intelligence significantly and positively influenced the adoption of big data analytics and blockchain and, in turn, firm performance. Additionally, the adoption of big data analytics and blockchain technology signified and positively mediated the relationship between business intelligence and firm performance. Both the mediations were partial. Finally, the study also provides managerial implications, limitations and future directions.
Construction projects’ performance is not self-regulating. Therefore, a continuous progress tracking and monitoring process is highly demanded to avoid potential deviations or misalignments. The current practice for the progress tracking and monitoring process suffers from heavily intermediated workflows, human errors, transfer latencies, inaccuracies, and/or information holes. Such issues could gradually lead to severe delays or even complete project failure. This research introduces a novel Peer-to-Peer (P2P) system that relies on Blockchain Technology (BT) and Inter-Planetary File System (IPFS) for managing progress information and as-built digital assets or files. The system is developed based on a three-step approach. First, two chaincodes are formulated for mapping and governing the data operations. Second, a private blockchain network is configured based on Hyperledger Fabric as a hosting platform, including the relevant stakeholders. Third, a private IPFS network is configured and coupled with a cluster service to manage and distribute the off-chain visuals and as-built digital assets. A case study for a non-residential construction project is utilized to test and verify the system’s practicability and assess its performance. The research significance is anticipated in diverse practical areas, including but not limited to; boosting coordination and trust among stakeholders, tracing progressive elaboration of As-built digital assets, accelerating incremental payments processing, assessing overall project performance and on-site productivity, supporting delay analysis and claim/dispute management, and streamlining data flow between the construction phase and the operation and maintenance phase. Further, the system’s future is mapped by evolving it as a sub-unit in a more advanced data model.
Alberto Arias Maestro, Óscar Sanjuán Martínez, Ankur Teredesai, Vicente García‐Díaz
Contemporary cloud application and Edge computing orchestration systems rely on controller/worker design patterns to allocate, distribute, and manage resources. Standard solutions like Apache Mesos, Docker Swarm, and Kubernetes can span multiple zones at data centers, multiple global regions, and even consumer point of presence locations. Previous research has concluded that random network partitions cannot be avoided in these scenarios, leaving system designers to choose between consistency and availability, as defined by the CAP theorem. Controller/worker architectures guarantee configuration consistency via the employment of redundant storage systems, in most cases coordinated via consensus algorithms such as Paxos or Raft. These algorithms ensure information consistency against network failures while decreasing availability as network regions increase. Mainstream blockchain technology provides a solution to this compromise while decentralizing control via a fully distributed architecture coordinated through Byzantine-resistant consensus algorithms. This research proposes a blockchain-based decentralized architecture for cloud resource management systems. We analyze and compare the characteristics of the proposed architecture concerning the consistency, availability, and partition resistance of architectures that rely on Paxos/Raft distributed data stores. Our research demonstrates that the proposed blockchain-based decentralized architecture noticeably increases the system availability, including cases of network partitioning, without a significant impact on configuration consistency.
The development of scientific and technological progress contributed to the formation of the so-called digital economy. The transformations affected, among other things, the monetary, financial and investment spheres. The emergence of bitcoin and other cryptocurrencies has expanded the list of investment assets. Today, not only independent investment is relevant, but also digital investment solutions are to be taken into account. One of the modern types of investments is investments in cryptocurrency mining. This article examines investments in the mining of one of the most famous cryptocurrencies - bitcoin. The difference between digital and cryptocurrency is clarified, the main sources of cryptocurrency are considered, the main attention is paid to mining. The study determines the profitability of bitcoin mining and compares the profitability of investing in bitcoin mining with the profitability of investing a similar amount of resources in a bank deposit.
Benjamin Hellenborn, Oscar Eliasson, İbrahim Yitmen, Habib Sadri
Purpose The purpose of this study is to identify the key data categories and characteristics defined by asset information requirements (AIR) and how this affects the development and maintenance of an asset information model (AIM) for a blockchain-based digital twin (DT). Design/methodology/approach A mixed-method approach involving qualitative and quantitative analysis was used to gather empirical data through semistructured interviews and a digital questionnaire survey with an emphasis on AIR for blockchain-based DTs from a data-driven predictive analytics perspective. Findings Based on the analysis of results three key data categories were identified, core data, static operation and maintenance (OM) data, and dynamic OM data, along with the data characteristics required to perform data-driven predictive analytics through artificial intelligence (AI) in a blockchain-based DT platform. The findings also include how the creation and maintenance of an AIM is affected in this context. Practical implications The key data categories and characteristics specified through AIR to support predictive data-driven analytics through AI in a blockchain-based DT will contribute to the development and maintenance of an AIM. Originality/value The research explores the process of defining, delivering and maintaining the AIM and the potential use of blockchain technology (BCT) as a facilitator for data trust, integrity and security.
For followers of the cryptocurrency industry—and who isn't nowadays—2022 did not end well and going into 2023 it doesn't appear much better. There is a dark cloud hanging over the industry. The barrage of negative news regarding cryptocurrency valuation and related technology concerns has no doubt created headwinds for the industry and those who have invested in cryptocurrency mining. Certainly, numerous questions have surfaced as to crypto's sustainability—both on the environmental front and financially, whether investors can rely on the technology's security. At the time of this writing, questions surrounding the latter have sent the crypto market reeling, with impacts on broader exchanges and investigations and indictments brought against one of the industry's largest cryptocurrency exchangers, FTX. And while numerous state and federal leaders are calling for more regulatory oversight of the industry on several fronts (we'll save the financial fallout for another editorial), the impacts of cryptocurrency mining on a changing climate are at the top of the list at federal, state, and local levels.
Julius Olatunde Omisola, Damodar Bihani, Andrew Ifesinachi Daraojimb, Grace Omotunde Osho · 6 authors
In today's complex global economy, ensuring transparency and traceability across supply chains has become a top priority for businesses, regulators, and consumers. Traditional supply chain management systems often suffer from data silos, manual errors, and delayed reporting, which compromise efficiency and accountability. This study proposes a conceptual framework for real-time data tracking and reporting using Blockchain and Artificial Intelligence (AI) to revolutionize supply chain transparency. The integration of Blockchain ensures immutable, decentralized, and tamper-proof data recording, while AI enables intelligent data analytics, anomaly detection, and predictive insights throughout the supply chain lifecycle. The framework is designed to enhance end-to-end visibility, improve trust among stakeholders, and optimize decision-making processes through continuous data synchronization and smart contracts. Key components of the framework include decentralized ledger infrastructure, AI-driven data processing engines, Internet of Things (IoT) sensor integration for real-time monitoring, and secure APIs for multi-stakeholder access. By combining these technologies, the framework facilitates seamless data flow across manufacturing, warehousing, transportation, and retail segments. Moreover, it enables real-time auditing, reduces the risk of fraud, and enhances compliance with environmental and ethical standards. Case studies in the pharmaceutical, food, and electronics industries highlight the applicability and scalability of the proposed model. The paper also addresses implementation challenges such as interoperability, data privacy, and the need for regulatory alignment. The conceptual model advocates for cross-industry collaboration and the standardization of digital supply chain practices through open-source protocols and blockchain consortiums. This framework contributes to academic and practical discourse by offering a transformative roadmap for supply chain digitization using emerging technologies. It underscores the potential of AI-enhanced blockchain solutions in ensuring transparency, boosting operational efficiency, and empowering consumers with verifiable product provenance data. The research concludes that adopting this model can significantly improve global supply chain resilience and accountability in an increasingly interconnected marketplace.
In the development of large-scale, integrated systems, the consistent clarity and distribution of knowledge and relevant status is crucial to success. There are many existing methods and commercial tools for providing context and traceability from requirements and specifications to a system's artifacts, but these tools are often vendor-locked and require access to cloud-based services and necessitate high licensing costs. Further, modern large-scale systems development involves multiple business partners, each of which needs to ensure their teams have granular, role-based access to all relevant information without impediment; centralized warehousing and gatekeeping should not be handled by a single entity if the information is to remain readily accessible. Instead, a permissioned, distributed knowledgebase that avoids vendor lock-in enables a consistent, real-time view of information that provides equivalent context for developers and other stakeholders. ChaordicLedger, a free and open source (FOSS) project joins the transparency and smart contract aspects of Distributed Ledger Technology with the storage capabilities of a Distributed File System to fulfill this industrial application while allowing for industry-specific customizations.
Universitas Negeri Malang Institut Teknologi dan Bisnis Asia Malang, Vivi Aida Fitria, Arif Nur Afandi, Aripriharta Aripriharta · 7 authors
Abstract. The non-fungible token (NFT) is a unique token used to represent digital assets such as art, music, videos, and other collections. NFT has gained significant attention from the business and industry sectors in recent years. This study reports an increase in the number of active NFT users from 77,000 to 222,000 in early 2021. Investment in NFT has advantages and disadvantages, and one of the challenges faced by investors is that they may not have enough knowledge about investing risks and may find it difficult to recognize and evaluate potential dangers. To address this problem, this study proposes a system that provides information on NFT collection sales rankings and volume sales forecasts. The simple additive weighting (SAW) method is used to determine the NFT collection rankings, and exponential smoothing is used to forecast sales volume. The Particle Swarm Optimization (PSO) method is applied to optimize the parameter alpha of the Exponential Smoothing method. With an accuracy rate of 80.38%, the combination of using the Single Exponential Smoothing method with PSO optimization can provide good predictions for future NFT sales. The proposed system aims to provide investors with accurate information to make informed decisions when investing in NFT. Keywords: Forecasting system, pso, ranking system, saw, single exponential smoothing
Oracles are software components that enable data exchange between siloed blockchains and external environments, enhancing smart contract capabilities and platform interoperability.Oracles play key roles in decentralized finance and blockchain applications in centralized finance.We find that integration into decentralized oracle networks is positively associated with key measures of economic activity such as Total Value Locked, triggered by positive network effects in adoption and usage.Our study reveals symbiotic gains from enhanced interoperability and network effects across protocols on a given chain and among integrated chains.Oracle integration appears to improve risk-sharing and mitigates contagion, increasing resilience during turbulent periods in crypto markets.Overall, oracles emerge as a crucial component to enable informational and economic integration in decentralized finance ecosystems.
Enterprises are still looking to achieve agility, scalability, and governance in their data architecture and have ended up facing a difficult problem. The monolithic nature of traditional designs is totally promising to that point but definitely cannot cope with demands of modern businesses, which are rapid in dynamic changes. The data mesh model enables a revolutionary new way of doing things by decentralizing data ownership and thus, the teams that are responsible for a certain domain have the power to treat the data as a product with clearly defined accountability for quality, accessibility, and usability. This transition not only allows federated governance but also benefits scalability and collaboration among domains. Carrying out a wide-scale implementation of a data mesh across an enterprise calls for using powerful and suitable tools and services, such as Apache Iceberg and Snowflake, which are very good in this area. Apache Iceberg is a great open table format for storage of big data quantities at the petabyte scale that comes with key features like schema evolution, time travel, and fast querying. It makes it easier to access and manage complicated datasets across various distributed computer systems, turning it into a perfect match for analytics of the modern era. Complemented by its cloud-native architecture, Snowflake is very good with Iceberg in that it can deliver unmatched performance, elasticity, and simplicity. Besides seamless processing of structured and semi-structured data, plus enabling features such as secure data sharing, and integrated governance, it ensures that data is the focus of the business. In unison, Snowflake and Iceberg build a framework that is decentralized but at the same time unified and that makes it possible for organizations to reap the benefits of a data mesh scale and as well utilize enterprise-grade performance and security. This tandem empowers domain teams to execute autonomous data management, which thus leads to innovation and expedited decision-making. Enterprises become able to forge a solid and future-proof data architecture by employing these technologies, one that naturally scales seamlessly, easily adjusts to changes, and enables teams to tap into the real value of their data
Researchers have started to recognize the necessity for a well-defined ML governance framework based on the principle of decentralization and comprehensively defining its scope of research and practice due to the growth of machine learning (ML) research and applications in the real world and the success of blockchain-based technology. In this paper, we study decentralized ML governance, which includes ML value chain management, decentralized identity for the ML community, decentralized ownership and rights management of ML assets, community-based decision-making for the ML process, decentralized ML finance, and risk management.
Anand Shankar Raja M., Benita Priyadarshini D., Janani Govindaraj, Saket Agarwal
Cryptocurrency is a commonly used term in the current world, and the COVID-19 pandemic has indirectly increased the awareness and the investor base for cryptocurrencies. Various research has been conducted to understand the complex working structure of these investment options and to analyse the volatile nature of cryptocurrencies. There are multiple factors and triggers that impact the price movements in the crypto market. Classifying these factors would help streamline the process of analysing these factors for further studies. These factors cause both positive and negative impacts on the price fluctuations. Classifying the major factors under the period of impact will help understand each factor's role in the market. This classification would help in the diagnostic and prescriptive analysis of cryptocurrencies. In this research, well-cited and published research papers, journals, and articles have been studied to classify some of the major factors affecting cryptocurrencies carefully. A model has been created to easily comprehend the classification of factors based on time of impact. This model simplifies the understanding of the factors and would help conduct further analysis on these factors.
Laura Corazza, Junru Zhang, Dilhani Kapu Arachchilage, Simone Domenico Scagnelli
This paper presents the implications of blockchain technologies on sustainability reporting and disclosure, and specifically proposes blockchain use-cases as a possible solution for problems experienced in the field of supply chain carbon information. This study addresses how the reliability of supply chains’ carbon-related information can become more transparent and reliable through a decentralized approach based on blockchain thinking (BT), issues that have been identified as a gap in the literature and in the practice. Scenario analysis and design science research (DSR) are used as a methodological driver to conceptualize over the nature of practical solutions using unified modeling language (UML) diagrams. The resulting use-case focuses on data retrieval in the supply chain. The paper also presents implications for the audit industry and their role in the assurance of such technological architecture implementations. The study is visionary as it offers a conceptualization based on scenario analysis. Developing a scenario enables researchers to depict a prospective situation, develop ability to solve future problems, and to back cast them in current policies, technologies, and actions.
The blockchain ecosystem has undergone three distinct periods of exponential growth — the ICO boom of 2017, the Defi summer of 2020, and most recently the NFT wave of 2021.However, it was not yet seen a “web3-native” approach towards NFTs from any major brand. By that, we mean that no brand has upended its Web 2.0 architecture and replaced it entirely with a web3 stack. Instead, brands have taken a more measured approach, exploring NFT drops, integrations, and community growth developed separately from their core offerings. This phase can be described as Web 2.5. In this article, the author attempts to define what Web 2.5 is concerning informatology, along with identifying the potential future challenges of information management in this area and the new iteration of digital development, namely Web 3.0 or web3.
With the deepening application of blockchain technology, exaggerating its empowering effects has become common. In recent years, the rational assessment of the maturity of blockchain technology applications in digital projects in different fields has been the focus of attention and identified as the key to improving the implementation effect of various digital projects. Although some studies have obtained substantial research results on technology maturity and its derivative applications, which can be used to predict the overall trend of a technology or guide the implementation of the technology on the ground, few studies have evaluated the maturity of blockchain technology in combination with different application scenarios. Our study combines application scenarios and the technical characteristics of blockchain technology and proposes an evaluation system for blockchain technology application maturity consisting of five primary indicators, that is, key application requirements, data security, process complexity, application ecological completeness, and technical performance requirements, and their corresponding secondary indicators. In addition, we take digital government public service projects as application scenarios and use the analytic hierarchy process (AHP) entropy method and expert scoring method to determine the weights corresponding to each index in the assessment system and construct a blockchain technology application maturity assessment model. Moreover, we apply the model to ten typical digital government public service projects to conduct a comprehensive assessment and analysis. By comparing the indicator scores of the different projects, we analyze the project characteristics influencing blockchain technology application maturity and provide suggestions for applying “blockchain + digital government public services”.