E-commerce Web Applications Research Articles

A Flexible Session-Based Recommender System for e-Commerce

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS tasks using different datasets. Our work contributes a number of very interesting findings. Among all tested models, LSTMs consistently outperform other methods of SBRS in all tasks. They can be applied directly because they do not need significant fine-tuning. Additionally, they naturally model the dynamic browsing that happens in e-commerce web applications. On the other hand, another important finding of our work is that graph-based methods can be a good compromise between effectiveness and efficiency. Another important conclusion is that a “temporal locality principle” holds, implying that more recent behavior is better suited for prediction. In order to evaluate these systems further in realistic environments, several session-based recommender methods were integrated into an e-shop and an A/B testing method was applied. The results of this A/B testing are in line with the experimental results, which represents another important contribution of this paper. Finally, important parameters such as efficiency, application of business rules, re-ranking issues, and the utilization of hybrid methods are also considered and tested, providing comprehensive useful insights into SBRS and facilitating the transferability of this research work to other domains and recommendation scenarios.

Features of microservices architecture in e-commerce systems

The development of information system architectures for e-commerce is a crucial task for busi-nesses in today's digital environment. This article presents a comparative analysis of different types of information system architectures used in e-commerce. The paper examines the specific characteristics of e-commerce and the requirements of web applications that support electronic business operations. Traditional monolithic architecture, microservices architecture, and server-less architecture have been characterized, and their advantages and disadvantages have been identified. Technological solutions in the field of e-commerce development have been studied to create an efficient and user-friendly platform. The article highlights such key architectural aspects as scalability, flexibility, deployment speed and management, reliability, and develop-ment and operational costs. The impact of each architecture type on performance and alignment with e-commerce business needs has been analyzed. This analysis will assist in the creation or modernization of information systems for e-commerce, enabling an informed choice of archi-tectural approach. The article specifically examines the advantages and disadvantages of micro-services architecture for developing e-commerce web applications, particularly using the Spring Boot framework. It explores how microservices can provide modularity and facilitate the devel-opment, deployment, and maintenance of e-commerce web applications. Such challenges of mi-croservices architecture as managing and coordinating services, as well as addressing data con-sistency and transaction management in distributed environments have been discussed. Some possible approaches and technical tools to overcome these challenges in the context of e-commerce web applications based on Spring Boot have been considered.

Analysis of the ergonomics of e-commerce websites

The following paper includes research about ergonomics of e-commerce web applications. Main purpose of experiment was to compare existing application of Morele.net shop and developed prototype of application using eyetracking examination and survey. The study carried out on a group of 40 students provided heat maps, scan paths, number of fixations and saccades, times to the first fixation in area of interest, task completion times, assessments of both applications in the form of WUP indicators. Based on the qualitative and quantitative analysis, conclusions were drawn confirming the hypothesis put forward in the work that there is an impact of ergonomic placement of navigation elements on the accessibility and usability of the application, as well as the time of performing tasks in it.

Dairy Farm

In today's world where every aspect of our lives if finding its way over the internet, it becomes a necessity for us to use the internet. The internet has been benefitting millions but certain portions of the society remains unversed with its advantages. Considering today's scenario, buying products by physically moving to places has become a real task for people. To provide an ease to this situation online e-commerce web applications are developed to provide an aid to the people in buying products at the comfort of their homes. On the same lines, the idea of this web application was developed . The basic idea of the whole project is to develop an android application similar to B2B websites and applications for a specific target product, i.e. the dairy products. This android application is built for a specific target product, which is of immense importance in our day to day lives. Every household requires milk and dairy products and there has not been any significant advancement in this specific area. This application tries to bridge the gap between the milk producers and their processors as they are often not in direct contact with each other and hence suffer due to these reasons. This will save their time and resources which are often wasted in the search for the right manufacturers for them. The application will benefit the producers, the processors as well as us being the users. The producers will benefit from the application as it will widen their reach and can even make them go global. On the other hand, The consumers can see and compare the rates and prices of the different dairy products from different producers and hence choose the most suitable one for them. The application will also try to incorporate the facility to contact the producers so that the consumers can reach them through either phone or message. A lot of factors will be taken into consideration while searching for products. The application will have two modes for its working: one for the consumers and other for the producers.

PELATIHAN PENGENALAN TEKNOLOGI CLOUD COMPUTING UNTUK MENINGKATKAN LITERASI DIGITAL GURU-GURU di SMK JP1 JAKARTA PUSAT

Cloud computing technology is one of the pillars of Information technology which is the foundation for the Industrial Revolution 4.0. Mastery of this technology is needed by prospective middle-class workers to be easily absorbed by the industry. SMK Jakarta Pusat 1, as a provider of human resources for software engineering skills, needs to update the knowledge and skills of teachers to meet these needs. The PKM team of Informatics Departemen of Universitas Trisakti held a cloud computing workshop to update the knowledge of SMK JP1 teachers. Pre-test and post-test were used to measure the knowledge level of participants. The training materials include various cloud computing services, the utilization of IaaS services, and the installation of e-commerce web applications on Digital Ocean's cloud services. The post-test results showed that the workshop participants could participate well in training. As many as 67% of the training participants gave a positive impression of this activity and expected more intensive training and a longer duration of time. The output of this training in the form of a training module is very relevant and can enrich the Cloud computing practice course at the Trisakti Informatics Engineering Department

Workload aware incremental repartitioning of NoSQL for OLTP applications

Numerous applications are deployed on the web with the increasing popularity of internet. These include gaming and e-commerce web applications. These applications generate huge amount of the data. One particular machine cannot handle such data. It is difficult to scale out by using relational databases. OLTP system needs to be scalable and require fast response. Therefore the scalability becomes the challenge for the e-commerce applications. Data partitioning technique is used to improve the scalability of the system. Existing partitioning techniques does not consider the relation among tuples. These techniques do not handle incremental data and are suitable for those applications that required only sequential access to the data. It results in increasing the number of the distributed transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions. Hypergraph representation technique represents transactional workload in graph form. In this technique, frequently used items are grouped together by using fuzzy C-means clustering algorithm. Tuple classification and migration algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.

Workload aware incremental repartitioning of NoSQL for OLTP applications

Numerous applications are deployed on the web with the increasing popularity of internet. These include gaming and e-commerce web applications. These applications generate huge amount of the data. One particular machine cannot handle such data. It is difficult to scale out by using relational databases. OLTP system needs to be scalable and require fast response. Therefore the scalability becomes the challenge for the e-commerce applications. Data partitioning technique is used to improve the scalability of the system. Existing partitioning techniques does not consider the relation among tuples. These techniques do not handle incremental data and are suitable for those applications that required only sequential access to the data. It results in increasing the number of the distributed transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions. Hypergraph representation technique represents transactional workload in graph form. In this technique, frequently used items are grouped together by using fuzzy C-means clustering algorithm. Tuple classification and migration algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.

Enhanced Recommendation System for E-commerce Applications

Recommendation algorithms play a quintessential role in development of E-commerce recommendation system, Where in Collaborative filtering algorithm is a major contributor for most recommendation systems since they are a flavor of KNN algorithm specifically tailored for E-commerce Web Applications, the main advantages of using CF algorithms are they are efficient in capturing collective experiences and behavior of e-commerce customers in real time, But it is noted that , this results in the phenomenon of Mathew effect, Wherein only popular products are listed into the recommendation list and lesser popular items tend to become even more scarce. Hence this results in products which are already familiar to users being discovered redundantly, thus potential discovery of niche and new items in the e-commerce application is compromised. To address this issue , this paper throws light on user behavior on the online shopping platform , accordingly a novel selectivity based collaborative filtering algorithm is proposed with innovator products that can recommend niche items but less popular products to users by introducing the concept of collaborative filtering with consumer influencing capability. Specifically, innovator products are a special subset of products which are less popular/ have received less traction from users but are genuinely of higher quality, therefore, these aforementioned products can be captured in the recommendation list via innovator-recognition table, achieving the balance between popularity and practicability for the user

Workload Aware Incremental Repartitioning of NoSQL for Online Transactional Processing Applications

<p><span lang="EN-US">Numerous applications are deployed on the web with the increasing popularity of internet. The applications include, 1) Banking applications,<br /> 2) Gaming applications, 3) E-commerce web applications. Different applications reply on OLTP (Online Transaction Processing) systems. OLTP systems need to be scalable and require fast response. Today modern web applications generate huge amount of the data which one particular machine and Relational databases cannot handle. The E-Commerce applications are facing the challenge of improving the scalability of the system. Data partitioning technique is used to improve the scalability of the system. The data is distributed among the different machines which results in increasing number of transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions and to reduce the number of transactions that are distributed in nature. Hyper Graph Representation technique is used to represent the entire transactional workload in graph form. In this technique, frequently used items are collected and Grouped by using Fuzzy C-means Clustering Algorithm. Tuple Classification and Migration Algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.</span></p>

Hybrid Recommendation System Using Clustering and Collaborative Filtering

A recommendation system is a way of suggesting users a subset of possible choice from a set of choices. An example of recommendation system in e-commerce web applications is to recommend customers what they might like to purchase from a wide variety of items based on their recent purchase behavior or search history. Recommender systems are redefining their meaning in today’s business oriented world by providing organizations an effective means of driving revenue. There are a lot of recommendation engines present at the moment. Some of them work solely on the user’s perspective whereas some work in generalized manner. There is a need that these recommender system start providing users with out of the box choices. They should not be limited to one’s individuality, rather other user preferences based on similarity should also be considered. Hence recommender systems play a major role in e-commerce domains and helps the business to achieve data intelligence. The proposed system explains various methods by which the user can be provided recommendations.

Performance Modeling of Multi-tiered Web Applications with Varying Service Demands

Multi-tiered transactional web applications are frequently used in IT enterprise based systems. Due to their inherent distributed nature, pre-deployment testing for high-availability and varying concurrency are important for post-deployment performance. Accurate performance modeling of such applications can help estimate values for future deployment variations as well as validate experimental results. In order to theoretically model performance of multi-tiered applications, we use queuing networks and Mean Value Analysis (MVA) models. While MVA has been shown to work well with closed queuing networks, there are particular limitations in cases where the service demands vary with concurrency. This variation of service demands for various resources (CPU, Disk, Network) is demonstrated through multiple experiments. This is further contrived by the use of multi-server queues in multi-core CPUs, that are not traditionally captured in MVA. We compare performance of a multi-server MVA model alongside actual performance testing measurements and demonstrate this deviation. Using spline interpolation of collected service demands, we show that a modified version of the MVA algorithm (called MVASD ) that accepts an array of service demands, can provide superior estimates of maximum throughput and response time. Results are demonstrated over multi-tier vehicle insurance registration and e-commerce web applications. The mean deviations of predicted throughput and response time are shown to be less the 3% and 9% , respectively. Additionally, we analyze the effect of spline interpolation of service demands as a function of throughput on the prediction results. Using Chebyshev Nodes , the tradeoff between the number of test points and the spline interpolation/prediction accuracy is also studied.Â

User Authentication in LabVIEW Platform

The increase in the information access terminals along with the growing use of information sensitive applications such as e-commerce, e-learning, e-banking, and e-healthcare and many more web applications have generated a real requirement of reliable, easy to use, and generally acceptable control methods for confidential and vital information. The person authentication is one of the most critical issues in contemporary societies. Accurate automatic personal identification is crucial to a wide range of application domains. It ensures that a system's resources are not obtained fraudulently by illegal users. So in any application the first stage is user authentication. King Khalid University (KKU) is a public university located at Abha in Asir region of Saudi Arabia. In this paper, we take a case study of KKU Department Administration system, a database application build in LabVIEW platform using database connectivity toolkit for authentication. The user supplies user name and password for authentication. If the user is a valid user then user is allowed further into the system otherwise not. The user authentication module is the backbone of any web based application. Keywords: Authentication, LabVIEW, VI, Block diagram, Front panel, Database connectivity toolkit

A Fraud Detection System Based on Anomaly Intrusion Detection Systems for E-Commerce Applications

The concept of exchanging goods and services over the Internet has seen an exponential growth in popularity over the years. The Internet has been a major breakthrough of online transactions, leaping over the hurdles of currencies and geographic locations. However, the anonymous nature of the Internet does not promote an idealistic environment for transactions to occur. The increase in online transactions has been added with an equal increase in the number of attacks against security of online systems. Auction sites and e-commerce web applications have seen an increase in fraudulent transactions. Some of these fraudulent transactions that are executed in e-commerce applications happen due to successful computer intrusions on these web sites. Although a lot of awareness has been raised about these facts, there has not yet been an effective solution to adequately address the problem of application-based attacks in e-commerce. This paper proposes a fraud detection system that uses different anomaly detection techniques to predict computer intrusion attacks in e-commerce web applications. The system analyses queries that are generated when requesting server-side code on an e-commerce site, and create models for different features when information is extracted from these queries. Profiles associated with the e-commerce application are automatically derived from a training dataset.

Investigating websites' e-CRM features in building customer relationships: evidence from Greece

In this study the 500 largest Greek companies are examined concerning their websites as marketing communication channels. Features of the websites are analysed and the most important features are categorised. Companies’ characteristics are analysed and connected to the business activities of the firms. Simultaneously, the companies are segmented using a two-step cluster analysis and, hence, the profiles of the clusters are presented. The findings indicate that Greek firms do not adopt advanced e-CRM features as a means to obtain feedback. Only a minority of firms surveyed have exploited the web as a marketing communication tool. Further, a field research was conducted addressed to the marketing managers of the largest 500 companies in Greece. The survey showed that the advanced e-CRM features are found to be related to the communication and advertisement goals of a company’s website. Moreover, those advanced features are related to the e-commerce web applications and information quality of the company’s webpage. Finally, some e-CRM strategy features and pursued characteristics of a company webpage, according to the marketing managers of the firms, are identified and presented.

A Generic Framework for Collecting and Mining Client Paradata for Web Applications

In this paper, we propose a general framework to track and collect user interactions with dynamic webpages. Using the AJAX, PHP, and MySQL technologies, we implement and realize the client-side-scripting framework to collect client paradata in a seamlessly manner. Being stored in a persistent storage at the server, the data were then structured and analyzed to understand the user behavior. We exploited the framework by applying it to two online systems: E-Survey and E-Commerce web applications. In the E-Survey system, we collected student interactions while filling in an online feedback form. We then used the collected data to model the user behavior. With the resultant model, we can infer whether a student is mindful and conscious while answering the feedback questions. In the E-Commerce system, we collected user interactions with a products page. Using a generated classifier, we can predict a user selection based on his or her navigation pattern on the page.

THE SECURITY POLICY FOR WEB-APPLICATION OF E-COMMERCE ACCORDANCE WITH THE REQUIREMENTS PA-DSS

The analysis of the requirements of the standard PA-DSS is provided. The main threats of e-commerce web-applications are analyzed. The security policy for Java-related web-appplication that performs payment func-tions by credit cards is formed. The choice of software to be used in the implementation of secure web-application is decided.

Freshness-driven adaptive caching for dynamic content Web sites

Both response time and content freshness are essential to e-commerce applications on the Web. One option to achieve good response time is to build a high performance Web site by deploying the state of art IT infrastructures with large network and server capacities. With such a system architecture, freshness of the content delivered is limited by the network latency since when users receive the contents, the contents may have changed at the server. With the wide availability of content delivery networks, many e-commerce Web applications utilize edge cache servers to cache and deliver dynamic contents at locations much closer to users, avoiding network latency. By caching a large number of dynamic content pages in the edge cache servers, response time can be reduced, benefiting from higher cache hit rates. However, this is achieved at the expense of higher invalidation cost. On the other hand, a higher invalidation cost leads to a longer invalidation cycle (time to perform invalidation check on the pages in caches) at the expense of freshness of cached dynamic content. In this paper, we propose a freshness-driven adaptive dynamic content caching technique, which monitors response time and invalidation cycle length and dynamically adjusts caching policies. We have implemented the proposed technique within NEC’s CachePortal Web acceleration solution. We have conducted experiments to evaluate effectiveness of the proposed freshness-driven adaptive dynamic content caching technique. The experimental results show that the proposed technique consistently maintains the best content freshness to users. The experimental results also show that even a Web site with dynamic content caching enabled can further benefit from deployment of our solution with improvement of its content freshness up to 10 times especially during heavy user request traffic and long network latency delay.