E-commerce Data Research Articles

Analysis of E-Commerce data breach and theft

The development in the field of E-commerce is something that is very important for our country today. E-commerce is not only advancing the country in the field of electronics, but this development can also be felt in other fields such as: Economics, and also the field of Education. However, it is not the only benefit that we can feel from these developments. There are things that can be very detrimental to us if we are not careful enough in the development of this E-commerce technology. Oneaofatheamostaimportant thingsato watch outafor is the breach of individual data/identity leakage that can be carried out by irresponsible parties in the E-commerce sector. Even though there are already articles of the ITE Law Article 32 Paragraphs 1, 2, and 3 which regulate the theft of personal data, the crime of theft still occurs in Indonesia. We can see examples of this crime from several similar crime cases, such as: The case of data leakage of 91 million accounts on Tokopedia in May 2020 and the case of data leakage of Cermati and Lazada involving 2.9 million accounts at the end of 2020.

E-Commerce Data Access Control and Encrypted Storage Based on Internet of Things

In order to discuss the access control and encrypted storage of Internet of Things data for e-commerce data, a method of Internet of Things data access control and encrypted storage based on optical fiber network communication is proposed. Build an IoT application model based on optical fiber network communication, which consists of a perception layer, a network layer, and an application layer. The network layer is through the optical fiber communication network. The information collected by the optical fiber sensor in the perception layer is transmitted to the application layer for processing. And the model-aware layer adopts the dynamic transformation encryption method for attribute decomposition, and the Internet of Things data access control strategy based on key management and implement IoT data access control and encrypted storage based on the IoT application model of optical fiber network communication. The experimental results show that when this method implements access control and encrypted storage of IoT data, it has the advantages of high efficiency, low storage space overhead, and low computational cost of fiber network nodes, and the security of the processed IoT data is as high as 97.77%. Conclusion. This method can effectively deal with the limited computing performance and storage performance of the perception layer nodes of the application model; larger and larger application requirements with a large number of nodes.

Research on Open Innovation Intelligent Decision-Making of Cross-Border E-Commerce Based on Federated Learning

Product development and innovation is the key issue for cross-border e-commerce operation. It is of great importance to build an intelligent open innovation system and maximize its proximity to market demand with the internationalization and digital advantages of cross-border e-commerce. Cross border e-commerce data is widely distributed among each node enterprise in the supply chain. But the enterprises will not share private data for intelligent learning for the sake of data security, which has become a difficulty problem in intelligent decision-making of open innovation. This paper analyzes the research status of open innovation and the technical basis of federated learning, builds an open innovation intelligent decision-making model of cross-border e-commerce based on federated learning, trains and tests the model using data from two participants, compares the intelligent prediction effects among whole learning, local learning and federated learning, and puts forward the collaborative promotion strategy of open innovation and intelligent optimization of cross-border e-commerce based on federated learning.

An E-Commerce Product Recommendation Method Based on Visual Search and Customer Satisfaction

Recently, extensive attention from researchers has been paid to users referring to product review comments when choosing products while shopping online. These shoppers are also more frequently demanding a visual search facility to identify similar or identical products based on images they input. In this paper, the authors propose a product recommendation method to support a visual product search that combines the similarities of both visual and textual information to recommend products with a high level of satisfaction. The authors first utilize the image-based recognition method to calculate the similarities between user-inputted images and product images based on their SIFT features and the surrounding text. Next, to select satisfying products, the authors perform sentiment analysis on product reviews and combine this with users' repeat purchase behavior to recommend products that have a high level of satisfaction rating. Finally, the authors evaluate and discuss the proposed method using real e-commerce data.

A Survey on: E-Commerce Data Analysis and Security Platform in the Era of Bigdata

With the rapid development of e-commerce and mobile communication, e-commerce platform has been widely used in various industries. How much e-commerce stores, whether it can guarantee the security of transaction information, whether it can analyze and study structured and unstructured data, and whether it can guarantee the security of stored data are all the key factors we need to consider. In this paper, the data and e-commerce security together, and to analyze the security system of e-commerce and discuss the prevention of hidden security policy. When the emergence of e-commerce big data technology can effectively solve the problems existing in e-commerce security, the Hadoop structure is introduced through Apache Hadoop and the Hadoop product Yarn is analyzed with emphasis. From the perspective of electronic security data, the hidden dangers of e-commerce can be effectively analyzed, and the security system of e-commerce can be effectively improved. This article starts with the analysis of the existing electronic commerce system, summarizes its characteristics, and analyzes and solves its existing problems. Firstly, the characteristics of the relational database My Structured Query Language (MySQL) and the distributed database HBase are analyzed, their respective advantages and disadvantages are summarized, and the advantages and disadvantages of each are taken into account when storing data. My SQL is used to store structured business data in the system, while HBase is used to store unstructured data such as pictures. These two storage mechanisms together constitute a data storage subsystem. Secondly, considering the large amount of data in the e-commerce system and the complex calculation of the data mining algorithm, this paper uses Map Reduce to realize the parallelization of the data mining algorithm and builds a Hadoop-based commodity recommendation subsystem on this basis. We use JavaEE technology to design a full-featured web mall system. Finally, based on the impact of cloud computing, mobile e-commerce is analyzed, including relevant theories, service mode, architecture, core technology, and the application in e-commerce, which can realize e-commerce precision marketing, find the optimal path of logistics, and take effective security measures to avoid transaction risks. This method can avoid the disadvantages of the traditional e-commerce, where large-scale data cannot be processed in a timely manner, realize the value of mining data behind, and realize the precision marketing of e-commerce enterprises.

Personal Data Protection to E-Commerce Consumer: What Are the Legal Challenges and Certainties?

Internet is one of the media facilities that is not only used for communication, but is also used in the process of buying and selling or trading (e-commerce). Behind all the conveniences obtained, e-commerce also raises the issue of consumer concerns on the responsibility for personal data. This study aimed to examine and re-explain the urgency of protecting personal data of e-commerce consumers and focused on the challenges and legal certainty of protecting personal data of e-commerce consumers in Indonesia. The method used was a doctrinal research method. This study showed that the application of data in e-commerce provides many benefits. However, the challenge was the absence of binding laws to safeguard personal data submitted to e-commerce companies. Legal certainty for the protection of e-commerce consumers is currently contained in several laws and regulations. Therefore, it is necessary to improve the effectiveness in the implementation of personal data protection and to regulate the protection of personal data in a law. In Indonesia, the urgency of the ratification of the Personal Data Protection Bill may become a solution in providing legal certainty for the protection of e-commerce consumers on their personal data.

RETRACTED ARTICLE: A comparative study of the market demand for Chinese proficiency test preparation books: evidence from e-commerce data

RETRACTED ARTICLE: A comparative study of the market demand for Chinese proficiency test preparation books: evidence from e-commerce data

Implementation of marketplace data in the production of Consumer Price Index in Indonesia

Digital Economy in recent years, especially in Southeast Asia, including Indonesia, is growing rapidly. E-commerce is one part of the Digital Economy. BPS-Statistics Indonesia as a Non-ministerial Government Agency responsible directly to the president has conducted an E-commerce Survey in 2019. From this publication, it is concluded that the interest of Indonesian traders using the internet in selling in recent years has increased. So, the urgency of using e-commerce data in its application in official Statistics is increasingly needed. Several studies have carried out the application of e-commerce data in the calculation of The Consumer Price Index (CPI). In this research, e-commerce data is applied with a case study using the data from one of online marketplaces in Indonesia in calculating CPI at city level in Java. The purpose of this study is to compare the marketplace-based CPI data and BPS-Statistics’ survey-based CPI. The data is collected through web scraping techniques and followed by preprocessing data and analyzed descriptively. Web scraper that is built can be used in obtaining data. Commodity-level CPI with marketplace data tends to have relatively large prices which result in higher CPI being compared to BPS-Statistics CPI. Meanwhile, at the expenditure group level, the CPI between the two approaches is broadly similar in general.

The e-commerce Use of E-Commerce in Increasing Sales of Small and Medium-Sized Micro Enterprises (MSMEs) Boutiques is Amid Pandemic Covid-19

The Covid-19 pandemic struck Indonesia and also the rest of the world, which has a huge impact on the Micro, Small, and Medium Enterprises (MSMEs). It is necessary to have a big movement to revive the economic passion in Indonesia, especially MSMEs, one of which is by utilizing e-commerce. E-Commerce is one of the means in the process of buying and selling products produced with a system that includes the use of the web online. The boutique is a fashion store that sells a variety of high-quality and exclusive clothing and accessories, customers can directly view the products being sold, register personal information, make orders, pay for goods. E-Commerce is created using the PHP programming language and MySQL database, using the Web Modeling Language (WebML) methodology. It was obtained that the E-Commerce data presented can increase the value of sales in the covid-19 pandemic with the use of E-Commerce makes it easier for consumers to choose items to be consumed or used without face-to-face or meeting between sellers and buyers. The online process is much more efficient compared to visitors coming directly to the store.
 Keywords: “E-Commerce”, “Web Modeling Language (Web ML).

Uncovering the identity of Electronic Markets research through text mining techniques

As an established academic journal in the e-commerce and digital platforms fields, Electronic Markets (EM) features a diverse range of topics and occupies a significant role in the information systems field. The study investigates EM’s topic diversity over the time period 2009–2020 using a text mining analysis and a bibliometric analysis and identifies 28 cluster groups. The analysis reveals that the top three topics are 1) service quality, 2) blockchain and other shared trust building solutions, their impact and credibility, as well as 3) consumer buying behavior and interactions. EM's core identity lies in a balanced set of core themes that bring technological, business or human/ social perspectives to the research of networked business and digital economy. This includes research on digital and smart services, applications, consumer behavior and business models, as well as technology and e-commerce data. Ethical and sustainability related topics are however still less present in EM.

Deep Neural Networks Applied to Stock Market Sentiment Analysis.

The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques.

Hunting for treasure: a systematic literature review on urban logistics and e-commerce data

ABSTRACT Online retail channels increasingly shape consumers’ purchase behaviour: we access a diversity of product types through web-shops; employ both smartphones and digital screens in stores; navigate the retail space by browsing online; and order pantry items, fresh groceries as well as prepared foods to be delivered at our doorsteps. The profound impact of online retail on mobility in cities, where the concentration of consumers resides, is, therefore, an extensively investigated and growing topic of interest in research. In the field of urban logistics, studies that evaluate the various impacts of e-commerce or propose efficiency or sustainability-enhancing applications are plentiful. Regardless, the general lack of solid urban e-commerce logistics data is supported widely. In this study, we systematically review the literature to identify and compare the types of e-commerce data that are currently known, employed and disclosed in urban logistics research as well as the data sources that provide access to them. Within the set of identified data, knowledge concentrates on consumer preferences and number of deliveries related to e-commerce. However, our findings confirm the general data paucity, specifically on delivery trip-related information such as deliveries per trip, number of delivery rounds and vehicle specificities. Discrepancies are found in methodologies to collect and compile data, as well as data units used (e.g., orders, parcels, deliveries) that cause large variations in information possibly diverging from reality. The study contributes to current literature and practice by compiling and analysing currently available data on urban e-commerce logistics and by presenting recommendations and best practices for future enhancements in this research field. Based on the systematic literature review, we propose a common data agenda for urban e-commerce logistics research, focused on addressing data gaps and topics that are under-developed and un-developed; pursuing data collection standardisation; disclosing data collection methodologies and sources; and specifying temporal and spatial information as well as units of data. Some data methodologies and sources can be recommended for future research: using interviews to collect quantitative data; collaborating with sector organisations; exploring open maps; employing existing household and time use surveys; and leveraging technological opportunities and new ways of collecting data.

Benefits of Availing Product Data Entry Services from eCommerce Data Entry Experts

Benefits of Availing Product Data Entry Services from eCommerce Data Entry Experts

The Implementation of K-Means dan K-Medoids Algorithm for Customer Segmentation on E-commerce Data Transactions

Nowadays, e-commerce data transactions are commonly used by companies to provide new information. The data transaction can reveal customer segmentation or groups based on the similar characteristics and behavior of each customer. Data Mining is one of technique to conduct the customer segmentation through clustering method. The study aims to applied the clustering method on e-commerce data transactions by using both K-Means and K-Medoids algorithm. The result shows that both algorithms reveal optimum of cluster result with value of k = 3. The results are also indicating the conformity with the elbow method’s results and the Davies Bouldin Index validity test which shows that the optimal number of clusters is 3. The test results show that K-Medoids has the best performance with a ration value of 0.337575 compared to K-Means 0.3380724. Hence, K-Medoids are used in data clustering as the optimal cluster. The results of customer segmentation according to the Customer Loyalty Matrix consist of core customers, new customers, and lost customers.

A Study on Resource Security under Legal Protection of e-Commerce Data Sovereignty Based on Blockchain Network

With the growing demand for cross-border electronic data forensics, national sovereignty and international network security have ushered in great challenges, and how to achieve dynamic balance in various fields under cross-border electronic data forensics, advance the process of global cyberspace governance, reach international consensus, and achieve win-win cooperation is a question that China needs to solve. Based on the above status quo, a blockchain-based secure e-commerce system is researched and implemented. Using the distributed architecture of the blockchain system and the automatic execution feature of smart contracts, a secure transaction application system is designed to realize decentralization and solve the problem of untrustworthy intermediate parties. Data storage is based on the mechanism of combining blockchain and offchain databases to ensure data security, and the characteristic of blockchain can only be appended and read to prevent the system from being tampered. The database index and the storage structure of smart contract commodity information and transaction information are established to improve the query efficiency. The functional and performance tests of the system show that the transaction processing efficiency is high in the multinode environment, and the response time of order and commodity status query can reach 80 ms. The design and development process of the system is introduced in detail, which is an important reference value for the application of blockchain technology in e-commerce.

Research on E-Commerce Data Standard System in the Era of Digital Economy From the Perspective of Organizational Psychology

With the rapid development of technology and the economy, the expansion of the network has had a huge impact on the rapid expansion of the industrial agglomeration e-commerce industry, as well as ensuring the shopping experience of consumers. The rapid expansion of industrial cluster e-commerce has avoided precisely the limitations of logistical bottlenecks. Current networks and modern information technologies can provide good support and maintain a huge growth potential. In addition, digital technologies such as multimedia are becoming increasingly important in industry cluster marketing, and the concept of industry cluster e-commerce models is gaining more and more attention from companies. However, virtual e-commerce systems under industrial clusters have not been well researched in the existing studies. In this paper, through extensive research, literature reading and website browsing statistics, the virtual e-commerce models of different industrial agglomerations are studied. Firstly, the concept of big data and the processing of big data are given. Secondly, the concept of industrial agglomeration and the relationship between industrial agglomeration and e-commerce are analyzed. The basic number of domestic Internet users in the last 10 years is also counted, proving that the expansion of the Internet has led to a substantial growth of Internet users in the country and that e-commerce plays a significant role in the future of business activities. Finally the study concludes that different e-commerce models have different performance and roles in industrial agglomeration e-commerce and cannot be generalized. Instead, it is not good and can only develop different industrial agglomeration e-commerce models according to different environments.

Data Analysis and Prediction Modeling Based on Deep Learning in E-Commerce

Due to the low efficiency of traditional data analysis methods for massive e-commerce data analysis, an e-commerce data analysis and prediction method based on the GBDT deep learning model was proposed. Purchase behavior is divided into another category, which transforms the problem of e-commerce data analysis and prediction into a binary classification problem. At the same time, we extract 107 features that can reflect the user behavior and construct the GBDT model. The characteristics include counting class, sorting class, time difference class, conversion rate class, and so on. It follows from the above that the analysis and prediction of e-commerce data are realized. In addition, the results show that when the learning rate of GBDT model parameters is 0.05, the number of basic learners is 200, the tree depth is 20, the threshold is 0.5, the model prediction effect is best, and the F1 value can reach 0.12. Compared with the traditional prediction model based on logistic regression and neural network, the proposed GBDT model is more suitable for e-commerce data analysis and prediction.

Enterprise Decision-Making and Analysis Based on E-Commerce Data Mining

Enterprises have established a number of business processing systems and business websites according to their own characteristics and business needs, such as e-commerce websites and shopping websites. Over time, a large number of sales transaction data and customer purchase information have been generated, but no useful information has been generated stored in the database. Therefore, the management and decision-making levels of enterprises try to get useful information from huge and complex data. With the development of network technology and database, data mining technology has emerged. Nowadays, data mining technology has become one of the most concerned technologies of e-commerce. It can select appropriate data mining methods according to the characteristics of commodities, carry out effective statistical analysis and decision support for data, predict and analyze future market trends, greatly improve the business intelligence analysis of e-commerce enterprises, and make enterprises have greater advantages in market competition. This paper explains some important methods related to data mining and designs a data mining system. One of the systems can help e-commerce decision-makers analyze and predict data. Through experiments, it is concluded that the average absolute percentage errors of prediction data are 8.6% and 5.3%, respectively, with small errors and high accuracy. Second, make better recommendations for users. After investigation and analysis, the highest satisfaction has increased by 25% after using the system.

Predicting Analysis of User’s Interest from Web Log Data in e-Commerce using Classification Algorithms

The accelerated development of e-commerce has been a concern for business people. Business people should be able to gain customer interest in a variety of ways so that their companies can compete with others. Analyzing click-flow data will help organizations or firms assess customer loyalty, provide advertising privileges, and develop marketing strategies through user interests. By understanding consumer preferences, clickstream data analysis may be used to determine who is participating, assist companies in evaluating customer contentment, boost productivity, and design marketing strategies. This research was performed by defining experimental user interests using Dynamic Mining and Page Interest Estimation methods. The findings of this analysis, using three algorithms at the pattern discovery page, demonstrated that the Decision Tree method excelled in both methods. It indicated that the operational performance of the Decision Tree performed well in the assessment of user interests with two different approaches. The findings of this experiment can be used as a proposal for researching the field of web usage mining, collaborating with other approaches to achieve higher accuracy values.

Design of E-commerce Data Scalable Storage System Based on Mobile Internet Communication Technology

The traditional encrypted storage system is inefficient when it encrypts the data of the Internet of Things, and there are few IOT data nodes that can be encrypted in a short time. In order to solve the above problems, a new Internet of Things data effective information encryption storage system is proposed. The hardware and software of the system are mainly designed. The chip selected for the collector is TTSAD251, which can expand the collection range. The processor is set with multiple cores to reduce the system power consumption. The memory uses SPRTAN-2 chip as the structure chip. The software work consists of three parts: collecting effective information of Internet of Things big data, establishing encrypted documents and storing effective information of big data of Internet of Things. In order to detect the working effect of the system, the experimental comparison with the traditional system shows that the proposed encryption storage system can improve the storage range of big data effective information of the Internet of Things by 20.58%, and the work efficiency by 5.64%. Compared with the traditional system, the designed system also has obvious advantages in the number of big data node secrets. In different files, the average number of big data information node encryption in this system is about 166,700. The experimental data show that the designed system has ideal application performance and provides a reliable basis for related fields.