Preprocessing and mining web log data for web personalization

One of the most challenging areas in human computer interaction is personalization. Nonetheless, usable and well design sites can not be replaced by personalization, which must be used in just measure. In order to avoid dealing with personal data from users and thus not to worry about privacy, we decide to work with anonymous data. Moreover, these data can be found in all web server logs. In this paper, we propose a methodology for simple personalization Amazon.com does with book recommendations based only on anonymous data about user's navigation in the website. Our target is to facilitate the navigation of users by means of suggested links towards the next page that users likely want to go. And this can be accomplished in real time using an intelligent agent system or at user requirements design by running a specific process in the system that offers instantaneously the suggested links.

2016

The growth of data available online and it's users are increasing exponentially and creating complexity for users to discover knowledge on the web. To improve performance of website web site designer should modify website according to users preferences and interests. To achieve this user's navigational behavior should be analyze which is captured in log files. First of all these log files are preprocessed and converted into suitable format for applying data mining techniques on it. Various DM techniques are apply on processed log files in order to get frequent access pattern of users of website. These patterns are analyzed and used extracted knowledge in website modification, system improvement, business intelligence, website personalization. In this paper, we provide detailed survey of work done so far on process of Web Usage Mining

Communications of the ACM, 2000

Industrial Engineering and Management Systems, 2012

In For the past few years, query log data has been collected to find user's behavior in using the site. Many researches have studied on the usage of query logs to extract user's preference, recommend personalization, improve caching and pre-fetching of Web objects, build better adaptive user interfaces, and also to improve Web search for a search engine application. A query log contain data such as the client's IP address, time and date of request, the resources or page requested, status of request HTTP method used and the type of browser and operating system. A query log can offer valuable insight into web site usage. A proper compilation and interpretation of query log can provide a baseline of statistics that indicate the usage levels of website and can be used as tool to assist decision making in management activities. In this paper we want to discuss on the tasks performed of query logs in pre-processing of web usage mining. We will use query logs from an online newspaper company. The query logs will undergo pre-processing stage, in which the clickstream data is cleaned and partitioned into a set of user interactions which will represent the activities of each user during their visits to the site. The query logs will undergo essential task in pre-processing which are data cleaning and user identification.

2013

Web server log repositories are great source of knowledge, which keeps the record of web usage patterns of different web users. The Web usage patt ern analysis is the process of identifying browsing patterns by analyzing the user's navigational behav ior. World Wide Web is a large amount of data provi der and a very large source of information. Users are increasing day by day for accessing web sites. For effective and efficient handling, web mining coupled with suggestion techniques provides personalized contents a t the disposal of users. Web Mining is an area of Data Mining dealing with the extraction of interesting kno wledge from the Web. Here we are presenting a personalization process based on Web usage mining. This paper reviews the process of discovering useful patterns from the web server log file. In this a host of Web usage mining activities required for this process, includ ing the pre-processing and integration of data from multiple sources, and common pattern disco...

2007

In this paper we focus on log data preprocessing, the first step of a common Web Usage Mining process. In particular, we present LODAP (LOg DAta Preprocessor), a software tool which we designed and implemented in order to perform preprocessing of log data. The working scheme of LODAP embraces several steps. Firstly, log files are cleaned by removing irrelevant data. Then, the remaining requests are structured into user sessions, encoding the browsing behavior of users. Successively, the uninteresting sessions and the least visited pages are removed in order to reduce the size of data concerning the previously extracted user sessions. In addition, LODAP allows to create reports containing the results obtained in each step and information summaries mined from the analysis of the considered log files. During the preprocessing through LODAP, the analyst is guided by a sequence of panels representing the wizard-based interface which characterizes the tool. Each panel is a graphical window which offers a basic function of the preprocessor. Preliminary results on log files of a specific Web site show that the implemented tool can effectively reduce the log data size and identify user sessions encoding the user browsing behavior in a significant manner.

It is the method to extract the user sessions from the given log files. Initially, each user is identified according to his/her IP address specified in the log file and corresponding user sessions are extracted. Two types of logs ie., server-side logs and client-side logs are commonly used for web usage and usability analysis. Server-side logs can be automatically generated by web servers, with each entry corresponding to a user request. Clientside logs can capture accurate, comprehensive usage data for usability analysis. Usability is defined as the satisfaction, efficiency and effectiveness with which specific users can complete specific tasks in a particular environment. This process includes 3 stages, namely Data cleaning, User identification, Session identification. In this paper, we are implementing these three phases. Depending upon the frequency of users visiting each page mining is performed. By finding the session of the user we can analyze the user behavior by the time spend on a particular page.

2012

Now a day's many web sites are growing and which indirectly leads to increase the complexity of web site designing. To simplify this we have to understand how web sites are being used, how navigation is done, and how many users use it and how much time user spends on pages. This is done using Web Usage Mining. Data sources to usage mining are client side cookies, web server log, software agent etc. This paper presents, how web server log data is preprocesses, which includes data cleaning, user identification and Sessionization, path completion. Once the data is preprocessed it is used for discovering some useful patterns.

International Journal of Computer Applications, 2015

The paper discusses about browsing behavior of the user and the user's interest, the technology of web mining, the information source from the system i.e. web log data and the apriori algorithm which is used. Web graph is generated by incorporating the behavior of user's browsing. The privacy is therefore not affected as the user's feedback is not collected. The paper also tells us how user's estimation of behavior is done based on studying web logs.

International Journal of Computer Applications, 2015

In The increasing craze of internet has geared a number of modern firms for using web technology in their day to day lives. A remarkable ability to analyze web log data is provided to them by the web mining technology, that are putatively full of information, but frequently lacking with meaningful information. This signifies the need to the development of an inference mechanism which is advanced and that draws out richer implication from mining's output. This paper presents a web mining algorithm that aims at amending the interpretations of the draft's output of association rule mining. This algorithm is being tremendously used in web mining. The results obtained prove robustness of the algorithm proposed in this paper.