ArchCollect - A Tool for WEB Usage Knowledge Acquisition from User's Interactions (original) (raw)
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A framework to discover, maintain and use the knowledge extracted from web browsing
Proceedings of the winter …, 2004
When a web site is visited, data about the visitor browsing are store in log files. Processing these files is possible to find hidden knowledge about the visitor behavior and using it, suggest changes in web site structure and content. Depending of web site's transactionally, the web log can contain millions of registers and part of them haven't relevant data. In this way, a methodology to clean data and get information is introduced. In this work a framework get and maintenance knowledge from web transaction is introduced ...
Users encounter with some troubles finding the information they need to access easily at the right time because on the one hand they need to examine the relevance of each page with their needs and on the other hand must assess reliability of pages. In recent decades retrieval systems and search engines have been created to fix this problem which index the content of web pages and pages relevant to the user query will be returned. Burdensome of information in current web is a major problem. Personalization systems were provided to deal with this problem which compatible the content and services of a web site based on interests and behavior of people. An essential element in any web personalization system is its user model. The content of web page can be used to create more precise models of the user , but approaches based on key words does not have a deep understanding of website. Nevertheless, manually creating a hierarchy of concepts is time-consuming and costly. On the other hand the public literal meaning resources are suffering from low coverage of specific phrases for domains. In this article we're going to resolve both of these defects. Our main achievement is providing a mechanism to improve the user views automatically in Web site using a comprehensive lexical meaning source. We are using today's largest encyclopedia Wikipedia as a rich source for meanings to improve automated modeling manufacture of user's interests. The proposed architecture includes a number of components that include: pre-primary processing, mining concepts website domain, extracting keywords from web site, creator of keywords vector and key words mapping to concepts. Another important achievement is using the structure of website to limit specific concepts of the domain.
An innovative data collection method to eliminate the preprocessing phase in web usage mining
Engineering Science and Technology, an International Journal, 2023
The underlying data source for web usage mining (WUM) is commonly thought to be server logs. However, access log files ensure quite limited data about the clients. Identifying sessions from this messy data takes considerable effort, and operations performed for this purpose do not always yield excellent results. Also, this data cannot be used for web analytics efficiently. This study proposes a method for user tracking, session management, and collecting web usage data. The method is mainly based on an innovative approach for using collected data for web analytics extraction as the data source in web usage mining. An application-based API has been developed with a different strategy from conventional client-side methods to obtain and process log data. The log data has been successfully gathered by integrating the technique into an enterprise web application. The results reveal that the homogeneous data collected and stored with this method is more convenient to browse, filter, and process than web server logs. This structured data can be used effortlessly as a reliable data source for high-performance web usage mining activity, real-time web analytics, machine learning algorithms, or a functional recommendation system.
Web Usage Mining Using Semantic Web Approach: A Study, Survey and Analysis
The ultimate aim of this research paper is to show the web usage mining using semantic web approach. Web data mining is an essential approach in data mining that allows user or machine to retrieves and analyzes the information from web in reflex manner. In fact it is an art of getting the desired result in terms of relevant information from World Wide Web. Web usage mining is one of the essential branch of web data mining. Semantic web helps in integration the data from different distributed environments or web resources. Web resources is organized in the form of metadata known as Resource Description Framework (RDF). RDF is Extended Markup Language (XML) based data model, where data is depicted in the form of triplets. In this paper we are including the study of KNIME tool to show web usage mining. KNIME (read as "naim") stand for Konstanz Information Miner, is developed by KNIME.com and written in Java. It is an open source data analytics, reporting and integration platf...
Approach for Processing of Web Usage Data
2016
The Web has recently become a powerful stand for retrieval of Information and discovering knowledge from web data. The Web mining is one of the applications of data mining techniques to depict the knowledge out from web log data. Web mining is generally defined as Preprocessing, discovery and analysis of useful information from the Web. Web Usage Mining consists as the process of Preprocessing, Pattern Discovery and pattern Analysis. The memory and time usage is compared by means of the pattern discovery algorithms such as Apriori and Frequent Pattern Growth algorithm. The aim of this paper is to understand the web usage mining process such as preprocessing of web usage data and also the finding of frequent Patterns and their analysis. And also the comparison of both algorithms on the same dataset is done. Due to more use of internet, the log files are increasing at higher rate in according to size. The Preprocessing plays an important role in efficient mining process because data i...