Transaction Frequency Based Trust for E-Commerce

Most traditional trust computing models in E-commerce do not take the transaction frequency among participating entities into consideration,which makes it easy for one party of the transaction to obtain a high trust value in a short time,and brings many disadvantages,uncertainties and even attacks.To solve this problem,a transaction frequency based trust is proposed in this study.The proposed method is composed of two parts.The first part is built on the classic Bayes analysis based trust modelswhich are ease of computing for the E-commerce system.The second part is the transaction frequency module which can mitigate the potential insecurity caused by one participating entity gaining trust in a short time.Simulations show that the proposed method can effectively mitigate the self-promoting attacks so as to maintain the function of E-commerce system.

Spatial Structure of China's E-commerce Express Logistics Network Based on Space of Flows

The intermediate link compression characteristics of e-commerce express logistics ne tworks influence the tradition al mode of circulation of goods and economic organization,and alter the city spatial pattern.Based on the theory of space of flows,this study adopts China Smart Logistics Network relational data to build China's e-commerce express logistics network and explore its spatial structure characteristics through social network analysis(SNA),the PageRank technique,and geospatial methods.The results are as follows:the network density is 0.9270,which is close to 1;hence,indicating that e-commerce express logistics lines between Chinese cities are nearly complete and they form a typical network structure,thereby eliminating fragmented spaces.Moreover,the average minimum number of edges is 1.1375,which indicates that the network has a small world effect and thus has a high flow efficiency of logistics elements.A significant hierarchical diffusion effect was observed in dominant flows with the highest edge weights.A diamond-structured network was formed with Shanghai,Guangzhou,Chongqing,and Beijing as the four core nodes.Other node cities with a large logistics scale and importance in the network are mainly located in the 19 city agglomerations of China,revealing the fact that the development of city agglomerations is essential for promoting the separation of experience space and changing the urban spatial pattern.This study enriches the theory of urban networks,reveals the flow laws of modern logistics elements,and encourages coordinated development of urban logistics.

Cryptocurrency Transaction Network Embedding From Static and Dynamic Perspectives: An Overview

Cryptocurrency, as a typical application scene of blockchain, has attracted broad interests from both industrial and academic communities. With its rapid development, the cryptocurrency transaction network embedding(CTNE) has become a hot topic. It embeds transaction nodes into low-dimensional feature space while effectively maintaining a network structure,thereby discovering desired patterns demonstrating involved users' normal and abnormal behaviors. Based on a wide investigation into the state-of-the-art CTNE, this survey has made the following efforts: 1) categorizing recent progress of CTNE methods, 2) summarizing the publicly available cryptocurrency transaction network datasets, 3) evaluating several widely-adopted methods to show their performance in several typical evaluation protocols, and 4) discussing the future trends of CTNE. By doing so, it strives to provide a systematic and comprehensive overview of existing CTNE methods from static to dynamic perspectives,thereby promoting further research into this emerging and important field.

Securing Stock Transactions Using Blockchain Technology: Architecture for Identifying and Reducing Vulnerabilities Linked to the Web Applications Used (MAHV-BC)

This paper deals with the security of stock market transactions within financial markets, particularly that of the West African Economic and Monetary Union (UEMOA). The confidentiality and integrity of sensitive data in the stock market being crucial, the implementation of robust systems which guarantee trust between the different actors is essential. We therefore proposed, after analyzing the limits of several security approaches in the literature, an architecture based on blockchain technology making it possible to both identify and reduce the vulnerabilities linked to the design, implementation work or the use of web applications used for transactions. Our proposal makes it possible, thanks to two-factor authentication via the Blockchain, to strengthen the security of investors’ accounts and the automated recording of transactions in the Blockchain while guaranteeing the integrity of stock market operations. It also provides an application vulnerability report. To validate our approach, we compared our results to those of three other security tools, at the level of different metrics. Our approach achieved the best performance in each case.

A Fair and Trusted Trading Scheme for Medical Data Based on Smart Contracts

Data is regarded as a valuable asset,and sharing data is a prerequisite for fully exploiting the value of data.However,the current medical data sharing scheme lacks a fair incentive mechanism,and the authenticity of data cannot be guaranteed,resulting in low enthusiasm of participants.A fair and trusted medical data trading scheme based on smart contracts is proposed,which aims to encourage participants to be honest and improve their enthusiasm for participation.The scheme uses zero-knowledge range proof for trusted verification,verifies the authenticity of the patient’s data and the specific attributes of the data before the transaction,and realizes privacy protection.At the same time,the game pricing strategy selects the best revenue strategy for all parties involved and realizes the fairness and incentive of the transaction price.The smart contract is used to complete the verification and game bargaining process,and the blockchain is used as a distributed ledger to record the medical data transaction process to prevent data tampering and transaction denial.Finally,by deploying smart contracts on the Ethereum test network and conducting experiments and theoretical calculations,it is proved that the transaction scheme achieves trusted verification and fair bargaining while ensuring privacy protection in a decentralized environment.The experimental results show that the model improves the credibility and fairness of medical data transactions,maximizes social benefits,encourages more patients and medical institutions to participate in the circulation of medical data,and more fully taps the potential value of medical data.

Throughput-oriented associated transaction assignment in sharding blockchains for IoT social data storage

Blockchain is a viable solution to provide data integrity for the enormous volume of 5G IoT social data, while we need to break through the throughput bottleneck of blockchain. Sharding is a promising technology to solve the problem of low throughput in blockchains. However, cross-shard communication hinders the effective improvement of blockchain throughput. Therefore, it is critical to reasonably allocate transactions to different shards to improve blockchain throughput. Existing research on blockchain sharding mainly focuses on shards formation, configuration, and consensus, while ignoring the negative impact of cross-shard communication on blockchain throughput. Aiming to maximize the throughput of transaction processing, we study how to allocate blockchain transactions to shards in this paper. We propose an Associated Transaction assignment algorithm based on Closest Fit (ATCF). ATCF classifies associated transactions into transaction groups which are then assigned to different shards in the non-ascending order of transaction group sizes periodically. Within each epoch, ATCF tries to select a shard that can handle all the transactions for each transaction group. If there are multiple such shards, ATCF selects the shard with the remaining processing capacity closest to the number of transactions in the transaction group. When no such shard exists, ATCF chooses the shard with the largest remaining processing capacity for the transaction group. The transaction groups that cannot be completely processed within the current epoch will be allocated in the subsequent epochs. We prove that ATCF is a 2-approximation algorithm for the associated transaction assignment problem. Simulation results show that ATCF can effectively improve the blockchain throughput and reduce the number of cross-shard transactions.

Approach to conceptual data integration for multidimensional data analysis in e-commerce

In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.

A Surfing Concurrence Transaction Model for Key-Value NoSQL Databases

As more and more application systems related to big data were developed, NoSQL (Not Only SQL) database systems are becoming more and more popular. In order to add transaction features for some NoSQL database systems, many scholars have tried different techniques. Unfortunately, there is a lack of research on Redis’s transaction in the existing literatures. This paper proposes a transaction model for key-value NoSQL databases including Redis to make possible allowing users to access data in the ACID (Atomicity, Consistency, Isolation and Durability) way, and this model is vividly called the surfing concurrence transaction model. The architecture, important features and implementation principle are described in detail. The key algorithms also were given in the form of pseudo program code, and the performance also was evaluated. With the proposed model, the transactions of Key-Value NoSQL databases can be performed in a lock free and MVCC (Multi-Version Concurrency Control) free manner. This is the result of further research on the related topic, which fills the gap ignored by relevant scholars in this field to make a little contribution to the further development of NoSQL technology.

Validating Intrinsic Factors Informing E-Commerce: Categorical Data Analysis Demo

Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research was reviewed; next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjectures about the independence of the socio-economic variables and e-commence were tested. Some of the factors influencing patronage of e-commerce were identified. It is clear from the literature that as one’s academic qualification improves, there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected patronage in e-commerce. Gender also appeared to affect patronage in e-commerce. 62.3% of staff had patronized e-commerce. This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe. It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.

Research on the Modern Precision E-commerce Marketing Model under the Big Data and Pattern Recognition Background

In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp the user consumption pattern and the basis of the electric business enterprise through the use of big data can be personalized, accurate and intelligent advertising push service, service mode for the creation of more interesting and effective. Under this basis, electricity companies can also pass the assurance of pair of big data, looking for better increase user stickiness, development of new products and services, the ways and methods to reduce operational costs and accordingly, we propose the novel perspectives on the corresponding issues for the systematic level enhancement that provides the novel methodology of precision e-commerce marketing.

Application of Big Data Analysis Technology in Cross-Border E-Commerce

Under the background of national development strategy in the new era,cross-border e-commerce with the help of Internet platfbnn can realize the interconnection between producers and consumers,and gradually expand the influence of international trade.Based on big data technology,this paper builds an industry chain with cross-border e-commerce members'participation,and analyzes the specific application of big data in the product support,internal operation,external marketing,logistics service and service evaluation of cross-border e-commerce industry chain.The purpose is to effectively promote the healthy development of cross-border e-commerce and improve China's trade and economic level.

Paths for Upgrade and Transformation of E-commerce of China's Fresh Agricultural Products Based on Whole Industry Supply Chain

China's e-commerce market scale of fresh agricultural products has reached 1000 billion yuan,and the prospect is bright.However,in the face of such problems as quality control,storage and distribution,and consumption habits,the fresh product e-commerce has great difficulty in development.Internet generates large data,and data are the resources and the core of the Internet and the foundation for survival and development of Internet.This paper analyzed the current situation of the development of fresh agricultural products,and discussed the plight of fresh product e-commerce in scale,profit,non-standard,and network environment.On the basis of the whole industry supply chain,it came up with paths for upgrade and transformation of the fresh product e-commerce with the big data as the core in the context of Internet environment.

MR-CLOPE: A Map Reduce based transactional clustering algorithm for DNS query log analysis

DNS(domain name system) query log analysis has been a popular research topic in recent years. CLOPE, the represented transactional clustering algorithm, could be readily used for DNS query log mining. However, the algorithm is inefficient when processing large scale data. The MR-CLOPE algorithm is proposed, which is an extension and improvement on CLOPE based on Map Reduce. Different from the previous parallel clustering method, a two-stage Map Reduce implementation framework is proposed. Each of the stage is implemented by one kind Map Reduce task. In the first stage, the DNS query logs are divided into multiple splits and the CLOPE algorithm is executed on each split. The second stage usually tends to iterate many times to merge the small clusters into bigger satisfactory ones. In these two stages, a novel partition process is designed to randomly spread out original sub clusters, which will be moved and merged in the map phrase of the second phase according to the defined merge criteria. In such way, the advantage of the original CLOPE algorithm is kept and its disadvantages are dealt with in the proposed framework to achieve more excellent clustering performance. The experiment results show that MR-CLOPE is not only faster but also has better clustering quality on DNS query logs compared with CLOPE.

The Roles of Information and Communication Technologies (ICTs) and E-Commerce as Agents of Nigeria’s Economic Development: Review of Challenges and Prospects

Electronic commerce which denotes the process of electronic transaction via internet has led to a very significant improvement in the level of growth, development, efficiency and productivity of global economies. In order to benefit from the economic opportunities offered by electronic commerce, Nigerian government and business organizations needs to effectively integrate Information and Communication Technologies (ICTs) as the major component of e-commerce campaign initiatives. The discussion in the paper is centered on e-commerce and ICTs and their resultant effect on Nigeria’s economic growth and development. This paper has presented an in-depth discussion of the various types of e-commerce, the major contributions of e-commerce to the economic growth of Nigeria and finally the challenges that impede the growth of e-commerce in Nigeria were identified and the possible recommendations for solutions to those challenges were provided.

Mining Metrics for Enhancing E-Commerce Systems User Experience

The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the need to measuring their quality using practical methods. This paper presents a quality evaluation framework for web metrics that are B2C specific. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined by ISO25010. Beginning from the existing large corpus of general-purpose web metrics, e-commerce specific metrics are chosen and categorized. Analysis results are subjected to a data mining analysis to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user defined meta-metrics.

Logistics Demand Forecast of Fresh Food E-Commerce Based on Bi-LSTM Model

Fresh products have the characteristics of perishable, small batch and high frequency. Therefore, for fresh food e-commerce enterprises, market demand forecasting is particularly important. This paper takes the sales data of a fresh food e-commerce enterprise as the logistics demand, analyzes the influence of time and meteorological factors on the demand, extracts the characteristic factors with greater influence, and proposes a logistics demand forecast scheme of fresh food e-commerce based on the Bi-LSTM model. The scheme is compared with other schemes based on the BP neural network and LSTM neural network models. The experimental results show that the Bi-LSTM model has good prediction performance on the problem of logistics demand prediction. This facilitates further research on some supply chain issues, such as business decision-making, inventory control, and logistics capacity planning.

数据交易安全刑事合规的“软法”治理及清单模式

数据交易安全合规指参与数据交易活动的企业内外部各方主体进行的维护数据安全合规的治理活动。刑事治理是由各方主体参与的惩治和预防犯罪的过程,具有过程性。数据交易安全刑事合规是刑事治理的重要方面,可分为事前合规和事后合规两个阶段。《信息安全技术数据交易服务安全要求(征求意见稿)》和《上海数据交易所数据交易安全合规指引》及配套清单采取正面清单和负面清单的管理模式,列举了数据交易主体、数据安全管理体系、数据来源、数据产品可交易性等方面的合规要求。数据交易安全合规清单具有“软法”性质,“软法”与“硬法”相结合,形成企业刑事合规的规范体系。刑法根据数据交易主体及行为场景的差异,以不同的罪刑规范保护数据交易行为所涉及的多元法益。数据犯罪的空白罪状具有定罪指引作用,也为企业合规的开展留下了空间。在实践中应注重数据安全关联罪名适用与刑行衔接,将企业刑事合规融入刑事司法过程并与行政监管相协调。同时,积极发挥企业合规清单的刑事治理机能,构建和完善激励机制、滤罪机制、评估机制,实现合规清单治理的出罪机能与治本效果。

数据概念的体系性建构

在推动数据合规交易的背景下,统一数据法律制度与数据交易实践中的数据概念愈发迫切。基于明确数据交易阶段的核心功能,可从历史视角建构以“行为—结果”为依据,以“数据要素—数据产品(数据商品或数据资产)—数据资本”为顺序的数据概念体系。在数据生产阶段,数据分级子概念对应的不同市场流通度决定了核心数据、重要数据、一般数据三级概念的选用;数据分类子概念所能引发的不同加工规则要求个人数据、企业数据、公共数据应以数据内容为分类标准。在数据交易阶段,以原有信息为内容的数据资源和以再生信息为内容的数据服务对应了不同的风险防范要求。

我国数据要素市场治理的模式、现状与对策

要推动数据要素市场高质量发展,首先需要构建科学高效的市场治理机制。本文对数据要素市场治理素现状进行了梳理,提出市场化治理、垂直型治理和关系型治理等三种治理模式,并比较其特点,分析其现状与瓶颈。在此基础上,本文指出我国需要同时采用多种治理模式,形成以市场化治理为主、垂直型治理为辅、关系型治理作为重要补充的数据要素市场治理格局,并就三种治理模式的进一步完善提出了对策建议。

公共数据进入数据要素市场模式研究

公共数据是数据要素市场的重要组成部分,其如何进入数据要素市场以及其参与数据要素市场的模式分为几类,学界对此少有研究。通过梳理和比较,深入挖掘国内外公共数据参与数据要素市场的模式类型,归纳得出公共数据进入数据要素市场有两大类、五种模式,包括一级市场(授权运营)+二级市场(场内交易)模式,公共数据开发利用+场外交易模式,依托数据平台构建公共数据流通市场模式,借助数据经纪商、数据中介的交易模式,以及通过(公共)数据信托参与数据市场模式等。在此归纳基础上,演绎出公共数据进入数据要素市场的理想模式,这一理想模式分为数据获取、数据产品生产、数据产品交易三个各有其独特特征的环节,可为我国公共数据进入数据要素市场的策略选择提供参考。