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Some issues in blockchain for accounting and the supply chain, with an application of distributed databases to virtual organizations

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INTELLIGENT SYSTEMS IN ACCOUNTING FINANCE & MANAGEMENT 2019年第3期26卷 137-149页

作者： O'Leary, Daniel E. Univ Southern Calif Los Angeles CA 90007 USA

This paper reviews some recent blockchain-based applications for information capture, distribution and preservation. As part of that review, this paper examines two key concerns with current blockchain designs for accounting and supply chain transactions: data independence and multiple semantic models for the same information distribution problem. Blockchain applications typically integrate database, application and presentation tiers all in the same ledger. This results in a general inability to query information in the ledger and other concerns. Further, since most applications appear to be private blockchain applications, there is a concern of agents needing to accommodate multiple blockchains depending on who their trading partners are and what they request. Finally, this paper uses a distributed database to design a 'blockchain-like' system for virtual organizations.

关键词： Arrow's impossibility theorem BigchainDB blockchain data independence design science distributed database private processes public processes semantic models virtual organizations

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AI-Assisted Dynamic Modeling for Data Management in a distributed System

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JOURNAL OF INTERCONNECTION NETWORKS 2022年第SUPP05期22卷 2147002-2147002页

作者： Tian, Yihao Sichuan Univ Sch Publ Adm Chengdu 610065 Sichuan Peoples R China

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure's functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

关键词： Artificial intelligence distributed database data management security management

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A prospective interoperable distributed e-Health system with loose coupling in improving healthcare services for developing countries

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ARRAY 2022年 13卷

作者： Rinty, Mahnuma Rahman Prodhan, Uzzal Kumar Rahman, Md. Mijanur Jatiya Kabi Kazi Nazrul Islam Univ Dept Comp Sci & Engn Trishal 2224 Mymensingh Bangladesh

A loosely coupled distributed system enables interoperability in healthcare structures and can be used to create flexible architecture. The key limitation in adopting electronic health (e-Health) services in developing countries is the health data interoperability problem. Most developing countries are being confronted with the utilization of e-Health systems for their health care services. This study aims to develop a prospective e Health system for developing countries to improve the effectiveness of health care services. This paper presents an enhanced e-Health framework using distributed memory and heterogeneous data handling mechanisms with loose coupling. The distributed e-Health has been designed using e-Health standards that are compatible with health level 7 (HL7) and can facilitate interoperability among health information systems. The Message Passing Interface (MPI) has been implemented to store regular backups of heterogeneous health data. The HL7 standards can also handle security threats. Data interchange from one e-Health system server to another has been conducted based on the experimental results. The distributed database has been made accessible from a remote server by using loosely coupled technology. It also provided the e-Health data recovery features and handled an extensive database through a distributed environment. According to the study, the developed system can deliver a low-cost service to the intended developing-country community effectively.

关键词： distributed database distributed e-Health system Electronic Health Record (EHR) Health level 7 (HL7) Interoperability Loose coupling

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Improved K-Means Clustering Algorithm for Big Data Mining under Hadoop Parallel Framework

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JOURNAL OF GRID COMPUTING 2020年第2期18卷 239-250页

作者： Lu, Weijia Nantong Univ Informat Ctr Affiliated Hosp Nantong Jiangsu Peoples R China

In order to improve the accuracy and efficiency of the clustering mining algorithm, this paper focuses on the clustering mining algorithm for large data. Firstly, the traditional clustering mining algorithm is improved to improve the accuracy, and then the improved clustering algorithm is parallelized to improve the efficiency. In order to improve the accuracy of clustering, an incremental K-means clustering algorithm based on density is proposed on the basis of K-means algorithm. Firstly, the density of data points is calculated, and each basic cluster is composed of the center points whose density is not less than the given threshold and the points within the density range. Then, the basic cluster is merged according to the distance between the two cluster centers. Finally, the points that are not divided into any cluster are divided into the clusters nearest to them. In order to improve the efficiency of the algorithm and reduce the time complexity of the algorithm, the distributed database was used to simulate the shared memory space and parallelize the algorithm on the Hadoop platform of cloud computing. The simulation results show that the clustering accuracy of the proposed algorithm is higher than that of the other two algorithms by more than 10%.

关键词： Improved k-means clustering algorithm Big data mining Hadoop parallel framework Shared storage space Parallel computing Parallelization distributed database

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A systematic review of the institutional success factors for blockchain-based land administration

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LAND USE POLICY 2023年 125卷

作者： Ansah, Barikisa Owusu Voss, Winrich Asiama, Kwabena Obeng Wuni, Ibrahim Yahaya Leibniz Univ Hannover Geodet Inst Fac Civil Engn & Geodet Sci D-30167 Hannover Germany Kwame Nkrumah Univ Sci & Technol Coll Art & Built Environm Dept Land Econ Kumasi Ghana King Fahd Univ Petr & Minerals Dept Construct Engn & Management Dhahran 31261 Saudi Arabia

The land administration systems have witnessed many innovative technologies in the attempt to revolutionalize the task of recording land and property rights. One such revolution is the transition toward blockchain-based land administration. Like most technologies, blockchain-based land administration projects are not immune to failures, early adopters have encountered challenges, and several pilot cases have failed. Institutional factors have proven to be issues that can hinder the successful adoption of technological innovations such as blockchain-based land administration. This study identifies and analyses institutional success factors for adopting blockchain-based land administration. A systematic literature review of 37 studies resulted in the extraction of 18 related institutional factors critical to adopting blockchain-based land administration projects. Using Scott's three-pillar framework, the factors were divided into three dimensions: regulatory, organisational and cultural environment. Despite the popular opinion that the regulatory environment is integral to adopting blockchain-based land administration, our review suggests that a mutual co-existence and interplay between all the fac-tors is key to the success of blockchain-based land projects. This study concludes that the adoption of blockchain-based land projects can be enhanced by understanding the institutional factors necessary to adopt blockchain-based land administration.

关键词： distributed database Innovative land administration Technology adoption Institutions Institutional framework Systematic review

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A blockchain-based method and system for trading technological achievements (take the Chinese market as an example) 2021

A blockchain-based method and system for trading technologic...

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2021 The 9th International Conference on Information Technology: IoT and Smart City

作者： Man Xiang Wenlin Zhang Fan Jiang Yuchong Fang Tao An Ken Lin Tianhao Wu Cai Zeng Adolescent Health Zhejiang Institute of Mechanical&Electrical Engineering China Zhejiang University China Automation control Zhejiang University China Industrial engineering Zhejiang University China

ISBN: (纸本)9781450384971

With the development of a new generation of information and network technology, the number and types of patent infringements continue to increase, and the types of patented products are also more extensive, which hinders the certification and confirmation of patented products, authorized transactions, and patent evaluation. However, the traditional centralized information system is difficult to meet the demand. Blockchain technology provides a shared ledger mechanism and lays the foundation for the construction of credible distributed transaction management. Therefore, this platform is based on blockchain technology to create a safe, stable, transparent and efficient patent service system to provide customers with accurate, objective and efficient quality services.

关键词： Blockchain Ethereum distributed database Patent Management Smart Contract

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An Efficient Framework for Mining Association Rules in the distributed databases

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COMPUTER JOURNAL 2018年第5期61卷 645-657页

作者： Goyal, Lalit Mohan Beg, M. M. Sufyan Ahmad, Tanvir Bharati Vidyapeeths Coll Engn Comp Sci & Engn New Delhi India Aligarh Muslim Univ Fac Engn & Technol Comp Engn Aligarh Uttar Pradesh India Jamia Millia Islamia Comp Engn New Delhi India

While mining the association rules in distributed database, overhead increases at each site because of linkage and dependency with other sites. Each site scans database not only for itself but for the neighboring sites also. In the most popular Count Distribution (CD) and Fast distributed Mining (FDM) algorithms, sites generate and scan the identical candidate itemsets. In the CD algorithm, sites generate candidate k + 1 itemsets using global frequent k-itemsets and in the FDM algorithm, sites generate using its own and neighboring sites heavy frequent k-itemsets. Most of the itemsets scanned by the CD algorithm are infrequent. These infrequent itemsets are not scanned in the FDM algorithm. Anyhow, in the FDM algorithm, some of the itemsets may be found frequent on neither of the sites but scanned on all the sites. In this paper, an efficient framework and an algorithm have been proposed for mining association rules in the distributed database. In the proposed framework, initially, overhead of each site for generating and scanning candidate itemsets for the neighboring sites is reduced. Later, a site either does not scan candidate k-itemset of neighboring site or postpone till its k + 1 itemsets are scanned.

关键词： distributed mining distributed algorithms distributed database frequent itemsets

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distributed in-memory data management for workflow executions

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PEERJ COMPUTER SCIENCE 2021年 7卷 e527页

作者： Souza, Renan Silva, Vitor Lima, Alexandre A. B. de Oliveira, Daniel Valduriez, Patrick Mattoso, Marta COPPE Fed Univ Rio de Janeiro Rio De Janeiro Brazil Univ Fed Rio de Janeiro Campus Duque Caxias Rio De Janeiro Brazil Fluminense Fed Univ Rio De Janeiro Brazil Univ Montpellier INRIA LIRMM CNRS Montpellier France IBM Res Rio De Janeiro Brazil Snap Inc Los Angeles CA USA

Complex scientific experiments from various domains are typically modeled as workflows and executed on large-scale machines using a Parallel Workflow Management System (WMS). Since such executions usually last for hours or days, some WMSs provide user steering support, i.e., they allow users to run data analyses and, depending on the results, adapt the workflows at runtime. A challenge in the parallel execution control design is to manage workflow data for efficient executions while enabling user steering support. Data access for high scalability is typically transaction-oriented, while for data analysis, it is online analytical-oriented so that managing such hybrid workloads makes the challenge even harder. In this work, we present SchalaDB, an architecture with a set of design principles and techniques based on distributed in-memory data management for efficient workflow execution control and user steering. We propose a distributed data design for scalable workflow task scheduling and high availability driven by a parallel and distributed in-memory DBMS. To evaluate our proposal, we develop d-Chiron, a WMS designed according to SchalaDB's principles. We carry out an extensive experimental evaluation on an HPC cluster with up to 960 computing cores. Among other analyses, we show that even when running data analyses for user steering, SchalaDB's overhead is negligible for workloads composed of hundreds of concurrent tasks on shared data. Our results encourage workflow engine developers to follow a parallel and distributed data-oriented approach not only for scheduling and monitoring but also for user steering.

关键词： Parallel workflow management systems Task scheduling User steering distributed database In-memory database

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Federated Learning on distributed Medical Records for Detection of Lung Nodules 15

Federated Learning on Distributed Medical Records for Detect...

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15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 15th International Conference on Computer Vision Theory and Applications (VISAPP)

作者： Baheti, Pragati Sikka, Mukul Arya, K., V Rajesh, R. ABV Indian Inst Informat Technol & Management Gwa Gwalior 474015 India

ISBN: (纸本)9789897584022

In this work, the concept of federated Learning is applied on medical records of CT scans images for detection of pulmonary lung nodules. Instead of using the naive ways, the authors have come up with decentralizing the training technique by bringing the model to the data rather than accumulating the data at a central place and thus maintaining differential privacy of the records. The training on distributed electronic medical records includes two models: detection of location of nodules and its confirmation. The experiments have been carried out on CT scan images from LIDC dataset and the results shows that the proposed method outperformed the existing methods in terms of detection accuracy.

关键词： Federated Learning distributed database Decentralized training Electronic Medical Records Blockchain

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Hybrid Blockchain Architecture for Cloud Manufacturing-as-a-service (CMaaS) Platforms with Improved Data Storage and Transaction Efficiency

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Procedia Manufacturing 2021年 53卷 594-605页

作者： Mahmud Hasan Kemafor Ogan Binil Starly Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC 27695 United States of America Department of Computer Science North Carolina State University Raleigh NC 27695 United States of America

Blockchain based decentralized Cloud Manufacturing-as-a-Service (CMaaS) platforms enable customers to gain access to a large capacity of manufacturing nodes over cryptographically secure networks. In recent times, the Ethereum network has emerged as a popular blockchain framework for providing provenance and traceability of proprietary manufacturing data in decentralized CMaaS. However, the Ethereum ecosystem was only designed to store and transmit low volume financial transaction data and little has been done to make it an efficient repository of large manufacturing data streams in CMaaS systems. In this paper, the authors build on their previous work and report the design, implementation, and validation of middleware software architectures that allow Ethereum based distributed CMaaS platforms to harness the benefits of the secure asset models of the Ethereum ecosystem and the immutable big data storage capabilities of the decentralized BigchainDB database platform. A novel hybrid blockchain architecture enabled by efficient communication protocols and blockchain oracles is proposed. This architecture allows the transfer and immutable storage of large manufacturing data streams onto global BigchainDB nodes allowing data rich manufacturing transactions to bypass the transaction fees of the Ethereum ecosystem. Additionally, a machine learning based time series inference model is proposed which enables the forecast of Ethereum gas price into the future. This allows the CMaaS platform smart contracts to judiciously assign gas price limits and hence save on transactions ensuing from transfer or creation of assets. The outcomes of this research show that the designed hybrid architecture can lead to the reduction of significant number of computational steps and hence transaction fees on Ethereum by offloading large volume data onto BigchainDB nodes. A Random Forest regressor based time series inference model has been shown to exhibit superior performance in the prediction

关键词： blockchain smart contracts distributed database time series regression lstm gas price prediction

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