Business Intelligence from Web Usage Mining

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient

Web Mining for Web Personalization

Web personalization is the process of customizing a Web site to the needs of specific users, taking advantage of the knowledge acquired from the analysis of the user\u27s navigational behavior (usage data) in correlation with other information collected in the Web context, namely, structure, content, and user profile data. Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. In this article we present a survey of the use of Web mining for Web personalization. More specifically, we introduce the modules that comprise a Web personalization system, emphasizing the Web usage mining module. A review of the most common methods that are used as well as technical issues that occur is given, along with a brief overview of the most popular tools and applications available from software vendors. Moreover, the most important research initiatives in the Web usage mining and personalization areas are presented

Exploring the concept of web site customization: applications and antecedents

While mass customization is the tailoring of products and services to the needs and wants of individual customers, web site customization is the tailoring of web sites to individual customers? preferences. Based on a review of site customization applications, the authors propose a model with four different levels standardization, adaptation, passive personalization, and active personalization). Each of these levels requires a different level of involvement of both the supplier and the customer. Based on an extensive review literature the authors then develop conceptual models of the determinants of site customization from both a customer?s and a supplier?s point of view. Both models contain the factors that determine the willingness of a party (customer or supplier) to get actively involved in web site customization. Some factors have a positive impact on the willingness to customize while others have a negative impact. Managers engaged in site customization projects should realized that site customization is not an undisputed topic. Its success will be context dependent. The presented conceptual models can be used to analyze the essentials of a particular context and to assess the potential of web site customization.

A Data-Driven Approach to Measure Web Site Navigability

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Web Usage Mining: An Implementation

Web usage mining is the area of data mining which deals with the discovery and analysis of usage patterns from Web data, specifically web logs, in order to improve web based applications. Web usage mining consists of three phases, preprocessing, pattern discovery,and pattern analysis. After the completion of these three phases the user can find the required usage patterns and use these information for the specific needs. In this project, the DSpace log files have been preprocessed to convert the data stored in them into a structured format. Thereafter, the general procedures for bot-removal and session-identification from a web log file, have been written down with certain modifications pertaining to the DSpace log files, in an algorithmic form. Furthermore, analysis of these log files using a subjective interpretation of a recently proposed algorithm EIN-WUM has also been conducted. This algorithm is based on the artificial immune system model and uses this model to learn and extract information present in the web data i.e server logs. This algorithm has been duly modified according to DSpace@NITR Website structure

The use of web analytics on a small data set in an online media company : shifter´s case study

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe primary struggle in data analysis is the lack of talent in performing relevant and fit-to-business analyzes that retrieve knowledge and provides concise and clear action plans to today’s startups and small enterprises that exist online. Tracking, knowing and understanding the navigational patterns of user behavior for a 3 month period collection and using an Excel spreadsheet tool obtained a context for each piece of content produced and published by Shifter, an online media company. Investigations made after acquiring Shifter’s data resulted in recommendations for rethink and redesign the editorial content of the business to answer different community’s needs

Modeling usage of an online research community

Although online communities have been thought of as a new way for collaboration across geographic boundaries in the scientific world, they have a problem attracting people to keep visiting. The main purpose of this study is to understand how people behave in such communities, and to build and evaluate tools to stimulate engagement in a research community. These tools were designed based on a research framework of factors that influence online participation and relationship development. There are two main objectives for people to join an online community, information sharing and interpersonal relationship development, such as friends or colleagues. The tools designed in this study are to serve both information sharing and interpersonal relationship development needs. The awareness tool is designed to increase the sense of a community and increase the degree of social presence of members in the community. The recommender system is designed to help provide higher quality and personalized information to community members. It also helps to match community members into subgroups based on their interests. The designed tools were implemented in a field site - the Asynchronous Learning Networks (ALN) Research community. A longitudinal field study was used to evaluate the effectiveness of the designed tools. This research explored people\u27s behavior inside a research community by analyzing web server logs. The results show that although there are not many interactions in the community space, the WebCenter has been visited extensively by its members. There are over 2,000 hits per day on average and over 5,000 article accesses during the observation period. This research also provided a framework to identify factors that affect people\u27s engagement in an online community. The research framework was tested using the PLS modeling method with online survey responses. The results show that perceived usefulness performs a very significant role in members\u27 intention to continue using the system and their perceived preliminary networking. The results also show that the quality of the content of the system is a strong indicator for both perceived usefulness of the community space and perceived ease of use of the community system. Perceived ease of use did not show a strong correlation with intention to continue use which was consistent with other studies of Technology Acceptance Model (TAM). For the ALN research community, this online community helps its members to broaden their contacts, improve the quality and quantity of their research, and increase the dissemination of knowledge among community members

REVIEW PAPER ON WEB PAGE PREDICTION USING DATA MINING

The continuous growth of the World Wide Web imposes the need of new methods of design and determines how to access a web page in the web usage mining by performing preprocessing of the data in a web page and development of on-line information services. The need for predicting the user’s needs in order to improve the usability and user retention of a web site is more than evident now a day. Without proper guidance, a visitor often wanders aimlessly without visiting important pages, loses interest, and leaves the site sooner than expected. In proposed system focus on investigating efficient and effective sequential access pattern mining techniques for web usage data. The mined patterns are then used for matching and generating web links for online recommendations. A web page of interest application will be developed for evaluating the quality and effectiveness of the discovered knowledge.   Keyword: Webpage Prediction, Web Mining, MRF, ANN, KNN, GA

Log Analysis of Mobile User Behavior for a Public-Facing Math e-Learning Site

Log analysis of our public-facing mathematicswebsite has indicated a rapid increase in mobile users as a resultof the increasing popularity of mobile devices. It was found thatthe behavior of mobile users is different from that of PC users forvisitation trends, the number of page views, and searchesconducted. These results suggest that our website should beoptimized for mobile devices