International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 3 Issue: 5 2997 - 3002 _______________________________________________________________________________________________ 2997 IJRITCC | May 2015, Available @ http://www.ijritcc.org _______________________________________________________________________________________ A Comparative Study of Different Log Analyzer Tools to Analyze User Behaviors S. Bhuvaneswari P.G Student, Department of CSE, A.V.C College of Engineering, Mayiladuthurai, TN, India. bhuvanacse8@gmail.com T. Anand Professor &Head, Department of CSE, A.V.C College of Engineering, Mayiladuthurai, TN, India. anandavcce@gmail.com Abstract - With the explosive growth of information available on internet, WWW become the most powerful platform to broadcast, store and retrieve information. As many people move to internet to gather information, analyzing user behavior from web access logs can be helpful to create adaptive system, recommender system and intelligent e-commerce applications. Web access log files are the files that contain information about interaction between users and the websites with the use of internet. It contains the details like User name, IP Address, Time Stamp, Access Request, number of bytes transferred, result status, URL that referred. To analyze such user behavior, a variety of analyzer tools exist. This paper provides a comparative study between famous log analyzer tools based on their features and performance. Keywords - Web access logs, User behavior, Log analyzer, World Wide Web. __________________________________________________*****_________________________________________________ I. INTRODUCTION The development of internet in recent decades made E-commerce websites to bring large records from users. Behavior information of the web users are concealed in the web access logs. It can be automatically created and maintained by the web server. This log file contains much information such as IP address, user name, time stamp, access request, result status, bytes transferred, etc. relative to the web user. Sample log file format is as follows: Traditionally, four types of logs available in web server: transfer log, agent log, error log and referrer log. First two are standard whereas the remaining is optional. To analyze those access logs, one should follow the sequential steps such as preprocessing, user identification, session identification followed by clustering. A large variety of techniques have been proposed to do this task. Another efficient way to extract the user behavior from log files is by making use of automated analysis tools. Web log analyzer software passes a server log file from a web server, and based on the values contained in the log file, derives indicators about when, how, and by whom a web server is visited. Usually reports are generated from the log files immediately, but the log files can alternatively be passed for a database and reports generated on demand. Features supported by log analysis packages may include "hit filters", which use pattern matching to examine selected log data. II. RELATED WORK Internet is used as information source and it is commonly known as web. Web is an open medium. Due to its Openness, it becomes tough for users to plough through the information [1]. It has been necessary to utilize automated tools to analyze and track the usage patterns. These make a need to create server-side and client-side intelligent systems that can effectively mine for knowledge [2].This can be done analyzing the web access logs which is stored in web servers. These logs enable the analyst to keep track the website and the user behavioral patterns [3]. Olfa nasraoui et al[4] proposed the Competitive Agglomeration for Relational Data (CARD) Algorithm , a clustering algorithm that is designed to organize user sessions into profiles, where each profile would highlight a particular type of user. Michael shmuli-scheuer et al [5] proposed a scalable user profiling framework that is based on feature selection, where user profiles are represented by the textual content consumed or produced by different users and the aim is to weigh user profile terms according to their capability of representing the user’s interests. For that purpose, a new feature selection method based on Kullback- Leibler (KL) divergence, tailored for the user behavior analysis task. But these methods became more complicated because the algorithms provided. Web analytic tools provide simple and effective solutions for the websites without involving any tough algorithms and methods [6].