Web Mining and Security in E-commerce

The world wide web is expanding, everyday huge amount of data is added to the web. Finding relevant information in the web is becoming a difficult task. Web Mining is the process of analysing and mining the web to find useful information. By web mining we extract information that are implicitly present in the web. Web Mining is classified into Web Content Mining (WCM), Web Structure Mining (WSM), Web Usage Mining (WUM) based on the type of data mined. Web Structure Mining analyses the structure of the web considering it as a graph. WSM can be used to rank pages present in the web, to improve the efficiency of search engines. This paper discusses about Web Mining, its types, and various ranking algorithms used in Web structure Mining.

This paper gives an introduction to Web mining, then describes Web Structure mining in detail, and explores the data structure used by the Web. This paper also explores different Page Rank algorithms and compare those algorithms used for Information Retrieval. In Web Mining, the basics of Web mining and the Web mining categories are explained. Different Page Rank based algorithms like PageRank (PR), WPR (Weighted PageRank), HITS (Hyperlink-Induced Topic Search), DistanceRank and DirichletRank algorithms are discussed and compared. PageRanks are calculated for PageRank and Weighted PageRank algorithms for a given hyperlink structure. Simulation Program is developed for PageRank algorithm because PageRank is the only ranking algorithm implemented in the search engine (Google). The outputs are shown in a table and chart format.

Applying Web usage mining for personalizing hyperlinks in Web-based adaptive educational systems

Users for online shopping are increasing day by day because of easy to get and time-saving property of online shopping. Having a proper understanding of users interest for certain type of product or different products for online shopping becomes important to create personalized service for a target market. An important property of successful e-commerce website is the ability to provide useful content at the right time to users. And because of all this, personalization techniques are introduced to create adaptive shopping application in which user interfaces change according to users interest. User’s behavior information is stored in web log files, and to get the information data mining techniques are used in which they use statistical characters to model users behavior and not considering the sequence of action performed by uses. It becomes helpful if we follow user’s session to understand complex user behavior. Therefore to eliminates all these issues this paper proposes a linear-temporal logic model checking approach for the analysis of structured e-commerce weblogs. If we consider a common way of mapping log records according to e-commerce structure, weblogs can be easily converted into event logs by which behavior of the user is captured. After getting users behavior by performing different predefine queries to identify different behavioral patterns that consider the different actions performed by a user during a session.

E-commerce is creating growth opportunities for e-retailers, especially during the Covid-19 outbreak. It increases the perceptions of both providers and customers toward their shopping convenience. In 2021, Vietnam became one of the fastest-growing countries among Southeast Asia’s Internet economies. However, both scholars and management practitioners have argued the inconsistent assessments of the factors affecting customer purchase behavior in online settings. Thus, this study aims to identify the critical factors influencing customer behavioral intention toward e-commerce websites that were adopted from well-established models and relevant literature. The online survey was the data collection method. All items were measured through a 5-point Likert scale. This study proposed and analyzed a conceptual framework using existing survey data from 312 samples in the context of online buying via e-commerce websites. Data were analyzed by using the statistical tool SPSS. The results indicated that factors including perceived usefulness, transaction security, perceived ease of use, trust, and subjective norm positively influence customer shopping intention. The results revealed the most prominent impact of the transaction security on customer purchase intention. Besides, perceived risk is a significant barrier in online shopping through e-commerce websites. The researchers have brought practical insights and made conceptual contributions by extending existing research on the technology acceptance model (TAM) and theory of planned behavior (TPB) regarding online shopping on the e-commerce website. The paper helps to validate and strengthen the importance of customer purchase intention in an e-shopping environment. The findings would help e-commerce providers be able to formulate specific strategies to increase customer usage significantly. Moreover, managers can focus their attention and resources on the greatest significant factors regarding their effects on behavioral intention.

Web is huge and dynamically increasing day by day with exponential rate. If query is little bit complex then often or sometimes it becomes difficult to get relevant information in response. Several search engines try to optimize the users query and/or search results to provide the relevant information. Also many websites are ecommerce sites which are also competing to attract their customers and some are recommendation based. Now almost all good sites are using web mining techniques to improve their performance. In this paper, an attempt has been made to cover the key aspects used in web mining research. Initially problems faced by users and/or owners have been highlighted. Further it discusses types of data and techniques used by different categories of web mining are discussed. Finally research issues and application areas have been discussed.

Commodities price of others e-supermarkets or online shopping systems are the most important data for the shopkeepers of shop online. This requirement becomes actuality because of the Web mining developing very fast. The Web mining algorithm from extracting directory tree of different Website, the commodities name on the Web page and commodities price based on participle are described in detailed. All of them depend on the researched of the participle algorithm. The implementation shows that the participle algorithm can get more than ninety nine percent of average full rate and accuracy rate. The error rate of price dynamic trend analysis is less than four percent. The results show as by this way can touch the shopkeepers' minds, and it can support the originality data for the commodities markets and dynamic trend analysis.

Web sites are one of the most important tools for advertisements in international area for universities and other foundation. The quality of a website can be evaluated by analyzing user accesses of the website. To know the quality of a web site user accesses are to be evaluated by web usage mining. The results of mining can be used to improve the website design and increase satisfaction which helps in various applications. Log files are the best source to know user behavior. But the raw log files contains unnecessary details like image access, failed entries etc., which will affect the accuracy of pattern discovery and analysis. So preprocessing stage is an important work in mining to make efficient pattern analysis. To get accurate mining results user's session details are to be known.In our proposed work, user's browsing behavior on a web page is considered with different modes of actions on client side to calculate web page browsing time with more precision and accuracy. It helps us to maintain the accuracy in finding frequent web usage patterns and administrators to evaluate its website usage more effectively. In our proposed work we considered t, t1, t2 three time limits which are subjective to the website management. These values should be selected effectively because if we select small value it will increase load on client side and if we select large value it will not provide good results.

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

Web Mining is the process of retrieving non-trivial and potentially useful information or patterns from web.Web mining is universal set of Web Structure Mining, Web Usage Mining and Web content Mining.This paper describes and compares these three categories.It also provides comparative statements of various page ranking algorithms with link editing, General Utility Mining and Topological frequency Utility Mining Model by taking constraints such as Web Mining activity, topology, Process, Weighting factor, Time complexity, and Limitations etc..This also helps in comparing WPs-Tree and WPs-Itree structures.

This survey report basically gives a comparison report on different page ranking algorithm on the basis of numerical analysis.Four popular algorithms used for this purpose PageRank, Weighted PageRank, PageRank using VOL and finally weighted PageRank using VOL.This report will give brief introduction of web mining concept because web mining is core content in page rank calculation .Many aspect related to the page ranking will be discussed by taking PageRank algorithm and its variation as a basis for this purpose.A detailed comparison table of PageRank, Weighted PageRank, PageRank using VOL and finally weighted PageRank using VOL will be used for better analysis.

Existing approaches to model innovation ecosystems have been mostly restricted to qualitative and small-scale levels or, when relying on traditional innovation indicators such as patents and questionnaire-based survey, suffered from a lack of timeliness, granularity, and coverage. Websites of firms are a particularly interesting data source for innovation research, as they are used for publishing information about potentially innovative products, services, and cooperation with other firms. Analyzing the textual and relational content on these websites and extracting innovation-related information from them has the potential to provide researchers and policy-makers with a cost-effective way to survey millions of businesses and gain insights into their innovation activity, their cooperation, and applied technologies. For this purpose, we propose a web mining framework for consistent and reproducible mapping of innovation ecosystems. In a large-scale pilot study we use a database with 2.4 million German firms to test our framework and explore firm websites as a data source. Thereby we put particular emphasis on the investigation of a potential bias when surveying innovation systems through firm websites if only certain firm types can be surveyed using our proposed approach. We find that the availability of a websites and the characteristics of the website (number of subpages and hyperlinks, text volume, language used) differs according to firm size, age, location, and sector. We also find that patenting firms will be overrepresented in web mining studies. Web mining as a survey method also has to cope with extremely large and hyper-connected outlier websites and the fact that low broadband availability appears to prevent some firms from operating their own website and thus excludes them from web mining analysis. We then apply the proposed framework to map an exemplary innovation ecosystem of Berlin-based firms that are engaged in artificial intelligence. Finally, we outline several approaches how to transfer firm website content into valuable innovation indicators.

In e-commerce websites, web mining web page recommendation technology has been widely used. However, recommendation solutions often cannot meet the actual application needs of online shopping users. To address this problem, this paper proposes an e-commerce web page recommendation solution that combines semantic web mining and BP neural networks. First, the web logs of user searches are processed and 5 features are extracted: content priority, time consumption priority, online shopping users' explicit/implicit feedback on the website, recommendation semantics and input deviation amount. Then, these features are used as input features of the BP neural network to classify and identify the priority of the final output web page. Finally, the web pages are sorted according to priority and recommended to users. This project uses book sales webpages as samples for experiments. The results show that this solution can quickly and accurately identify the webpages required by users.

The web usage mining uses data mining techniques to discover interesting usage patterns from web data. Web personalization uses web usage mining techniques for the process of customization. Customization involves knowledge acquisition done by analysis of user's navigational behavior. A user when goes online would like to get the links which suits his requirements or usage in the website he visits. The next business requirement in the online industry will be personalizing/customizing the web page fulfilling for each individuals requirement. The personalization of the web page will involve clustering of different web pages having common usage pattern. As the size of the cluster goes on increasing due to increase in users or growth of interest of users it will become inevitable need to optimize the clusters. This paper proposes a cluster optimizing methodology based on ants nestmate recognition ability and is used for eliminating the data redundancies that may occur after the clustering done by the web usage mining methods. For clustering an ART1-neural network based approach is used. "AntNestmate approach for cluster optimization" is presented to personalize web page clusters of target users.

Data and web mining involves the use of data mining techniques to extract information from the web in order to gain insights into online behavior, customer preferences, and trends. The process requires data preprocessing to clean and prepare the data for analysis, and pattern recognition to identify relationships and patterns within the data. The benefits of data and web mining include improved decision-making and business growth, while challenges include the large volume of data and ensuring data quality. Web mining can be divided into three main categories: web content mining, web structure mining, and web usage mining. Web content mining involves extracting information from the content of web pages, such as text, images, and videos. Web structure mining involves analyzing the links between web pages to identify patterns and relationships. Web usage mining involves analyzing user behavior on the web, such as clickstream data and session logs, to extract useful information. KEYWORDS: data mining, web mining, information extraction, data preprocessing, pattern recognition, online behavior, customer preferences,