Web Mining for Public E-Services Personalization

2009 ◽  
pp. 1079-1086 ◽  
Author(s):  
Penelope Markellou ◽  
Angeliki Panayiotaki ◽  
Athanasios Tsakalidis
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

Web Mining for Public E-Services Personalization

2010 ◽  
pp. 751-758
Author(s):  
P. Markellou
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

Web Mining for Public E-Services Personalization

2011 ◽  
pp. 1629-1634
Author(s):  
P. Markellou
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Personalization ◽  
Business Transactions ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Over the last decade, we have witnessed an explosive growth in the information available on the Web. Today, Web browsers provide easy access to myriad sources of text and multimedia data. Search engines index more than a billion pages and finding the desired information is not an easy task. This profusion of resources has prompted the need for developing automatic mining techniques on Web, thereby giving rise to the term “Web mining” (Pal, Talwar, & Mitra, 2002). Web mining is the application of data mining techniques on the Web for discovering useful patterns and can be divided into three basic categories: Web content mining, Web structure mining, and Web usage mining. Web content mining includes techniques for assisting users in locating Web documents (i.e., pages) that meet certain criteria, while Web structure mining relates to discovering information based on the Web site structure data (the data depicting the Web site map). Web usage mining focuses on analyzing Web access logs and other sources of information regarding user interactions within the Web site in order to capture, understand and model their behavioral patterns and profiles and thereby improve their experience with the Web site. As citizens requirements and needs change continuously, traditional information searching, and fulfillment of various tasks result to the loss of valuable time spent in identifying the responsible actor (public authority) and waiting in queues. At the same time, the percentage of users who acquaint with the Internet has been remarkably increased (Internet World Stats, 2005). These two facts motivate many governmental organizations to proceed with the provision of e-services via their Web sites. The ease and speed with which business transactions can be carried out over the Web has been a key driving force in the rapid growth and popularity of e-government, e-commerce, and e-business applications. In this framework, the Web is emerging as the appropriate environment for business transactions and user-organization interactions. However, since it is a large collection of semi-structured and structured information sources, Web users often suffer from information overload. Personalization is considered as a popular solution in order to alleviate this problem and to customize the Web environment to users (Eirinaki & Vazirgiannis, 2003). Web personalization can be described, as any action that makes the Web experience of a user personalized to his or her needs and wishes. Principal elements of Web personalization include modeling of Web objects (pages) and subjects (users), categorization of objects and subjects, matching between and across objects and/or subjects, and determination of the set of actions to be recommended for personalization. In the remainder of this article, we present the way an e-government application can deploy Web mining techniques in order to support intelligent and personalized interactions with citizens. Specifically, we describe the tasks that typically comprise this process, illustrate the future trends, and discuss the open issues in the field.

Performance Comparison of Data Mining Classifiers on Web Log Data

2020 ◽  
Vol 17 (11) ◽  
pp. 5113-5116
Author(s):  
Varun Malik ◽  
Vikas Rattan ◽  
Jaiteg Singh ◽  
Ruchi Mittal ◽  
Urvashi Tandon
Keyword(s):  
Web Mining ◽  
Performance Comparison ◽  
Web Usage Mining ◽  
Classification Algorithms ◽  
Web Content ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.

A Heuristic Mining Algorithm Using Web Hyperlink Structure

2010 ◽  
Vol 108-111 ◽  
pp. 11-16
Author(s):  
Chun Lai Chai
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Usage Mining ◽  
Web Content ◽  
Web Content Mining ◽  
Web Structure ◽  
Mining Algorithm ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.

Analyzing Website Quality Issues through Web Mining

2017 ◽  
pp. 168-198
Author(s):  
G. Sreedhar
Keyword(s):  
World Wide Web ◽  
Web Mining ◽  
World Wide ◽  
Web Content ◽  
Website Quality ◽  
Web Structure ◽  
Web Structure Mining ◽  
The World ◽  
Content Mining ◽  
The Web

In the present day scenario the World Wide Web (WWW) is an important and popular information search tool. It provides convenient access to almost all kinds of information – from education to entertainment. The main objective of the chapter is to retrieve information from websites and then use the information for website quality analysis. In this chapter information of the website is retrieved through web mining process. Web mining is the process is the integration of three knowledge domains: Web Content Mining, Web Structure Mining and Web Usage Mining. Web content mining is the process of extracting knowledge from the content of web documents. Web structure mining is the process of inferring knowledge from the World Wide Web organization and links between references and referents in the Web. The web content elements are used to derive functionality and usability of the website. The Web Component elements are used to find the performance of the website. The website structural elements are used to find the complexity and usability of the website. The quality assurance techniques for web applications generally focus on the prevention of web failure or the reduction of chances for such failures. The web failures are defined as the inability to obtain or deliver information such as documents or computational results requested by web users. A high quality website is one that provides relevant, useful content and a good user experience. Thus in this chapter, all areas of website are thoroughly studied for analysing the quality of website design.

Web Mining

2011 ◽  
pp. 239-249 ◽  
Author(s):  
Roderick L. Lee
Keyword(s):  
Online Community ◽  
Web Mining ◽  
Local Level ◽  
Relevant Information ◽  
Web Content ◽  
Hub And Spoke ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
The Web

This chapter presents an overview of web mining. The three areas of web mining—Web content mining, Web usage mining, and Web structure mining—are identified. In this chapter specific attention is paid to Web structure mining, which is the study of the link topology. The link topology of the Web is analyzed in the context of a cyber-community in order to explore the connection between the link topology and conferral of authority. Millions, soon to be billions, of people are annotating Web documents, which results in an abundance of information. Herein lies the problem: topic distillation—searching through the sea of documents for relevant information. To address the problem of overabundance and relevancy, models are needed that can assist in creating order at the local level. The hub and spoke model identified in this chapter takes a proactive approach to creating an online community in a centralized or planned fashion and provides control over the architecture of the Web graph. In the end users can be assured with a certain level of confidence that the Web content contained in a hyperlinked community is both accurate and relevant.

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

2017 ◽  
pp. 298-314 ◽  
Author(s):  
G. Sreedhar ◽  
A. Anandaraja Chari
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Content ◽  
Web Data ◽  
Web Data Mining ◽  
Useful Knowledge ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining

Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.

Web Mining Overview

2011 ◽  
pp. 2085-2089 ◽  
Author(s):  
Bamshad Mobasher
Keyword(s):  
Web Mining ◽  
Relevant Information ◽  
Content Management ◽  
Heterogeneous Data ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
E Learning ◽  
Intelligent Tools

In the span of a decade, the World Wide Web has been transformed from a tool for information sharing among researchers into an indispensable part of everyday activities. This transformation has been characterized by an explosion of heterogeneous data and information available electronically, as well as increasingly complex applications driving a variety of systems for content management, e-commerce, e-learning, collaboration, and other Web services. This tremendous growth, in turn, has necessitated the development of more intelligent tools for end users as well as information providers in order to more effectively extract relevant information or to discover actionable knowledge. From its very beginning, the potential of extracting valuable knowledge from the Web has been quite evident. Web mining (i.e. the application of data mining techniques to extract knowledge from Web content, structure, and usage) is the collection of technologies to fulfill this potential. In this article, we will summarize briefly each of the three primary areas of Web mining—Web usage mining, Web content mining, and Web structure mining— and discuss some of the primary applications in each area.

DSS for Web Mining Using Recommendation System

2017 ◽  
pp. 22-34 ◽  
Author(s):  
Varaprasad Rao M ◽  
Vishnu Murthy G
Keyword(s):  
Recommender Systems ◽  
Web Mining ◽  
Recommendation System ◽  
User Behavior ◽  
Web Content ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining ◽  
Computer Based ◽  
The Many

Decision Supports Systems (DSS) are computer-based information systems designed to help managers to select one of the many alternative solutions to a problem. A DSS is an interactive computer based information system with an organized collection of models, people, procedures, software, databases, telecommunication, and devices, which helps decision makers to solve unstructured or semi-structured business problems. Web mining is the application of data mining techniques to discover patterns from the World Wide Web. Web mining can be divided into three different types – Web usage mining, Web content mining and Web structure mining. Recommender systems (RS) aim to capture the user behavior by suggesting/recommending users with relevant items or services that they find interesting in. Recommender systems have gained prominence in the field of information technology, e-commerce, etc., by inferring personalized recommendations by effectively pruning from a universal set of choices that directed users to identify content of interest.

Discover Patterns from Web-Based Dataset

2017 ◽  
pp. 78-106
Author(s):  
Raghvendra Kumar ◽  
Priyanka Pandey ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Web Site ◽  
Web Mining ◽  
Research Work ◽  
Web Content ◽  
Web Based ◽  
Web Structure ◽  
Information Matching ◽  
Varied Range ◽  
Conceptual Link ◽  
The Web

The Web can be defined as a depot of varied range of information present in the form of millions of websites dispersed around us. Often users find it difficult to locate the appropriate information fulfilling their needs with the abundant number of websites in the Web. Hence multiple research work has been conducted in the field of Web Mining so as to present any information matching the user's needs. The application of data mining techniques on web usage, web content or web structure data to find out useful data like users' way in patterns and website utility statistics on a whole can be defined as Web mining. The main cause behind development of such websites was to personalize the substance of a website on user's preference. New methods are developed to deal with a Web site using a link hierarchy and a conceptual link hierarchy respectively on the basis of how users have used the Web site link structure.

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