I couldn't stop looking at it, even though I've got other things to do The notes for my AAAI tutorial are here.
Usage data captures the identity or origin of Web users along with their browsing behavior at a Web site.
Web usage mining itself can be classified further depending on the kind of usage data considered: The user logs are collected by the Web server. Typical data includes IP address, page reference and access time.
Commercial application servers have significant features to enable e-commerce applications to be built on top of them with little effort. A key feature is the ability to track various kinds of business events and log them in application server logs.
New kinds of events can be defined in an application, and logging can be turned on for them thus generating histories of these specially defined events. It must be noted, however, that many end applications require a combination of one or more of the techniques applied in the categories above.
Studies related to work  are concerned with two areas: Costa and Seco demonstrated that web log mining can be used to extract semantic information hyponymy relationships in particular about the user and a given community.
Pros[ edit ] Web usage mining essentially has many advantages which makes this technology attractive to corporations including government agencies. This technology has enabled e-commerce to do personalized marketingwhich eventually results in higher trade volumes.
Government agencies are using this technology to classify threats and fight against terrorism. The predicting capability of mining applications can benefit society by identifying criminal activities.
Companies can establish better customer relationship by understanding the needs of the customer better and reacting to customer needs faster.
Companies can find, attract and retain customers; they can save on production costs by utilizing the acquired insight of customer requirements. They can increase profitability by target pricing based on the profiles created. They can even find customers who might default to a competitor the company will try to retain the customer by providing promotional offers to the specific customer, thus reducing the risk of losing a customer or customers.
More benefits of web usage mining, particularly in the area of personalizationare outlined in specific frameworks such as the Probabilistic Latent Semantic Analysis model, which offer additional features to the user behavior and access pattern. These models also demonstrate a capability in web usage mining technology to address problems associated with traditional techniques such as biases and questions regarding validity since the data and patterns obtained are not subjective and do not degrade over time.
The most criticized ethical issue involving web usage mining is the invasion of privacy. Privacy is considered lost when information concerning an individual is obtained, used, or disseminated, especially if this occurs without their knowledge or consent.
De-individualization, can be defined as a tendency of judging and treating people on the basis of group characteristics instead of on their own individual characteristics and merits. The growing trend of selling personal data as a commodity encourages website owners to trade personal data obtained from their site.
The companies which buy the data are obliged make it anonymous and these companies are considered authors of any specific release of mining patterns.
They are legally responsible for the contents of the release; any inaccuracies in the release will result in serious lawsuits, but there is no law preventing them from trading the data.
Some mining algorithms might use controversial attributes like sex, race, religion, or sexual orientation to categorize individuals. These practices might be against the anti-discrimination legislation.
This process could result in denial of service or a privilege to an individual based on his race, religion or sexual orientation. Right now this situation can be avoided by the high ethical standards maintained by the data mining company.
The collected data is being made anonymous so that, the obtained data and the obtained patterns cannot be traced back to an individual. Web structure mining[ edit ] You can help by adding to it. June Web structure mining uses graph theory to analyze the node and connection structure of a web site.
According to the type of web structural data, web structure mining can be divided into two kinds:Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners.
Call for Technical Research papers. The APSEC technical research track invites high quality submissions of papers describing original research studies and results in . Welcome to the 10th CyberC, Zhengzhou, October 18 - 20, China!
CyberC – International Conference on Cyber-enabled distributed computing and knowledge discovery – is to promote research and collaboration on the cyber-related technology. Abstract The optimal web data mining analysis of web page structure acts as a key factor in educational domain which provides the systematic way of novel implementation towards real-time data with different level of implications.
expand the potentials for decision support by finding styles and This volume contains 19 research papers. Chapter 21 Web Mining — Concepts, Applications, and Research Directions Jaideep Srivastava, Prasanna Desikan, Vipin Kumar Web mining is the application of data mining techniques to extract knowledge.
There is the possibility for conference attendees to publish the expanded materials of the conference in Special Issue of Journal DATA (ISSN ) – Open Access Journal of ʻData in Scienceʼ (Special Issue: Data Stream Mining and Processing).