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Web usage mining is the research area of web mining is used to predict the web user behavior interacts with the website. The objective of mining is to find the users access. This paper provides an area of web usage mining, including research efforts as well as commercial offerings. An up-to-date survey of the existing work is also provided. This chapter provides an overview of the state of the art in research of web usage mining, while discusses the most relevant criteria for deciding on the suitability of these techniques for building an adaptive web site.
The web is a huge and hard to estimate the growth of web data every day, the web provides different kind of services such as government, electronic commerce, news, etc. Mining of the web is interesting and potentially useful patterns of implicit information from World Wide Web. Web usage mining supports for the creation of web site design, providing personalization server and other business making decision, etc. Web usage mining consists of four steps. The first step is data collection, the second step is preprocessing, the third step is pattern discovery and the final step is pattern analysis. Web usage mining is the application of data mining.
WEB USAGE MINING
Bulent Ozel et al.,  proposed hybrid of web link prediction. Growing web is raising the navigational problems. Using web usage can predict the user behavior and help the designer to improve the design for attracting the user's usage. In this paper the author used both association rule mining technique and Markov chain model. The processes of hybrid link cluster the similar pages for increasing the efficiency of the proposed model. Yoon Ho Cho et al.,  presented the fast growing of e-commerce shows the customer demand on the web. In this paper e-commerce caused the overload of the customer on the web in a long time. To overcome this overload, varies method have been used. The author used the Collaborative filtering is the method has recommended overcoming the limitations on the existing method. Rana et al.,  focused a techniques could predict the behavior of the user's while interacting with the website. The approach is multi-disciplinary that the web is continued in growing. Thus can able to predict the user need to make them robust, scalable and efficient design. The data generated by surfer's sessions or user behaviors. They discussed the tools available in the applications of web usage mining and concluded with the challenges and future trends in the research. Zaïane et al. (1998) developed the knowledge discovery WeblogMiner tool from the server log files. Thus can improve the system performance, and enhance the quality website and deliver the useful data to the end users.
A Study of Web Usage Mining Research Tools is given by Chhavi Rana et al.,  the author presented the requirement of today's world and listed some challenges and issues of future trends of the users getting attention to the quality website. Abraham et al.,  has combined the data mining and the World Wide Web for research. The knowledge discovery could attempt to obtain from the secondary data. The author used i-miner tool and to optimize the concurrent architecture of a fuzzy clustering algorithm for discovering information cluster to analyze the trends the fuzzy inference is used. Cooley et al.,  described the web usage mining interesting patterns can be discovered from the uninteresting order. Several research efforts have relied of uninteresting rules. The Web Site Information Filter (WebSIFT) system usages the web content and web structure information from the website to identify the interesting patterns for mining frequent item sets from the real world.Web usage mining is the application of data mining techniques to discover the patterns from web, in order to understand the web-based applications. Data on the web can be mined from secondary data on the web. We could classify the data that reside on the web . INSITE Shahabi et al.,  To track the user interaction with the web and produce user profile in real time by the use of Connectivity Matrix Model (CM-Model), that shows the efficiency and scalability of the user's participatory attributes on the web to visualize the user navigation path in real time.By using graph the web usage data could represent . Frequently the web usage mining methods uses some background knowledge such as web content, website topology, Hierarchies, user navigation and constraints. In this paper the author proposed a new CF-based recommendation methodology that addressing the product overload problem in large E-commerce sites . A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites by Olfa Nasraoui et al.,  the author presented a complete framework to find the user profiles for discovering patterns from the log files of the original website. The user based interest is analyzed on the website by search queries to extract the web log data. LChen et al.,  developed WebMate; a proxy agent that helps to the user as effective browsing and searching on the web. The author experimentally described the existing and proposed systems of user profile used in various business and application point of view. A survey on web usage mining has been done by Koutri, Avouris, and Daskalaki .Building the user model by using the WebLogMiner techniques used for uncovering the hidden patterns within the web. Web access information is stored in the data sources. The author used these techniques for building the adaptive web site.SEWeP Eirinaki M. et al.,  discussed the needs of the user by analysis of the navigational behavior on the web. In order to personalization the author developed as a system that makes both the web usage logs and web site contents that encapsulates knowledge discovery from the link semantics. Buchner et al.,  introduced new algorithm called MiDAS in the wide range of web can discover the traditional sequential discovery. Thus shows the functionality as well as scalability. Ling et ai.,  Direct marketing is the process of identify the product who purchased and who sold. Data mining tool is used for direct marketing.
Web related information is appropriate and popular target for knowledge discovery, the knowledge discovery process concerns the web content, web structure and web usage. From the user interest the quality website can develop and discover the rules and patterns for taking some interesting measures . Senkul and Salin et al.,  generated the web usage mining techniques for web site restructuring and recommendation. The author investigated the semantic data of the web page on the patterns are generated for frequent sequences. The frequent user navigational pattern is measured by mechanism involving web page recommendation. SpeedTracer et al.,  developed user surfing behavior by server log files. The author found the user session by reconstructing the traversal path through referrer page and generated three types of reports are prepared. Rao, Kumari, and Raju,  developed association rule mining with incremental method. The author used this algorithm to suit the dynamic changing log scenario with incremental techniques. This technique is more efficient to run a number of databases. Ujwala Patil et al., proposed a survey or future request prediction of the extraction from web log files .Web is the fast rising research area the log files is very useful to predict the behavior of the user in different ways. They provided the past, current evaluation and updating of web usage mining. Extraction of Business Rules from Web logs to Improve Web Usage Mining is given by Sawan Bhawaret al., . The author presented the automatic data extracted by querying, organizing and analyzing. Information Extraction is the task of extracting structured information from unstructured one. Input is the log files and extracted the information from session performed by the user. This can improve the web usage. The author proposed the website with the limited web pages can easily predict the user demand from the particular web sites.
APPLICATIONS AND FUTURE TRENDS OF WEB USAGE MINING
Web usage mining has various application areas such as web prefetching, site reorganization, web personalization, system improvement, link prediction, Business Intelligence and Usage Characterization.
The performance of the system is very important for user satisfaction. Web usage mining is an important research area for detecting the web traffic. The communications between PC and the server constitute Web traffic. The amount of traffic and the details of each visit are extremely valuable information to a Web-based business. The server computer records every request for a Web page by user, and determines which pages get the most attention. Web traffic analysis gives businesses concrete, reliable information on the interests of their customers. The more traffic a Web site receives, the more sessions and hits its server processes. Every time a Web server processes a file request, the computer makes an entry in a server log, a dedicated file on the server's hard. To develop the server performance new policies can be used. Downloading files can slow down the user experience. If a user scrolls through an application screen and has to wait for content to load, the application appears slow to them. To use prefetching effectively, you need to evaluate the content application uses in order to determine meaningful indexes that identify which content is appropriate for prefetching. This prefetching approach is useful for both client and server level web caching, load balancing, transmission of data distributed information are the applications of web mining.
Attractiveness of the website is an important one gives good structure of the website. The principle of website reorganization is first need to understand how users interact with web-sites, how they think and what the basic patterns of users' behavior. In website navigation, the structure of the website can rearrange. The relationships between web pages are dynamically updated. Reorganization can be performed with the extraction of frequent patterns of web usage mining. The web usage information gives the information about the user behavior's of any website. Both content and structure leads to adaptive web site.
Web site personalization is based on usage information. Personalization depends on the gathering and use of personal user information, privacy issues are a major concern. The Personalization Consortium is an international advocacy group organized to promote and guide the development of responsible one-to-one marketing practices. The technologies behind personalization include: Collaborative filtering, in which a filter is applied to information from different sites to select relevant data that may apply to the specific e-commerce experience of a customer or specific group of customers. User profiling, using data collected from a number of different sites, which can result in the creation a personalized Web page before the user has been formally. Data analysis tools used to predict likely future interactions. Each page request is sent through the proxy, this will track the session across multiple web sites and marks interesting links.
The major system improvement life cycle is planning, analysis, development and implementation. It should support the user demand to build a system. Developing the system with the security can avoid the intrusion and to restrict the user's access to certain online contents. Understand the customer demand and retaining the customized products. Improve some satisfaction with the help of browsing behavior.
Link prediction is used for analyzing the nodes in a network, from the large network suggest that information can be extracted from the network topology.
Web usage mining provides data to improve the customer, sales and marketing field. It is the technology to access the data from various data sources, for business advantage, the data is gathered, stored and analyzed in organization can improve the customer needs and demands. Some decision about the business can be made to success. Thus the disciplines of Business intelligence includes decision support, data mining, online analytical processing (OLAP), querying and reporting, statistical analysis and forecasting.Some of the business intelligence tools are BizzScore Suite, IBM Cognous Series 10,WebFOCUS,QlikView,Tableau Software, Style Intelligence, Board Management Intelligence Toolkit, AS Enterprise BI Server can retrieve, analyze and generate report.
The usage of web used by the user is for various purposes. By characterizing the data usage of heavy users and normal users, and classify them and clusters according to their usage activities. The user behavior can be observed by usage regularities on the website. Characterize the users by navigational patterns and agent based approach.
This paper insight the possibility of merging data mining techniques with logs for achieving a web usage mining and web application evaluations.