• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1601-1614
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• 2008

The number of web service users has been increased rapidly as existing services are changed into the web-based internet applications. Therefore, it is necessary for us to use web log pre-processing technique to detect attacks on diverse web service transactions and it is also possible to extract web mining information. However, existing mechanisms did not provide efficient pre-processing procedures for a huge volume of web log data. In this paper, we proposed both a field based parsing and a high-speed log indexing mechanism based on the suggested B-tree Index Vector structure for performance enhancement. In experiments, the proposed mechanism provides an efficient web log pre-processing and search functions with a session classification. Therefore it is useful to enhance web attack detection function.

• KIISE Transactions on Computing Practices
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• v.21 no.10
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• pp.651-657
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• 2015

The amount of web traffic has increased as a result of the rapid growth of the use of web-based applications. In order to obtain valuable information from web logs, we need to develop systems that can support interactive, flexible, and efficient ways to analyze and handle large amounts of data. In this paper, we present CERES, a log-based, interactive web analytics system for backbone networks. Since CERES focuses on analyzing web log records generated from backbone networks, it is possible to perform a web analysis from the perspective of a network. CERES is designed for deployment in a server cluster using the Hadoop Distributed File System (HDFS) as the underlying storage. We transform and store web log records from backbone networks into relations and then allow users to use a SQL-like language to analyze web log records in a flexible and interactive manner. In particular, we use the data cube technique to enable the efficient statistical analysis of web log. The system provides users a web-based, multi-modal user interface.

• Journal of Internet Computing and Services
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• v.11 no.6
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• pp.73-86
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• 2010

In proportion to the rapid increase in the number of Web users, web attack techniques are also getting more sophisticated. Therefore, we need not only to detect Web attack based on the log analysis but also to extract web attack events from audit information such as Web firewall, Web IDS and system logs for detecting abnormal Web behaviors. In this paper, web attack detection system was designed and implemented based on integrated web audit data for detecting diverse web attack by generating integrated log information generated from W3C form of IIS log and web firewall/IDS log. The proposed system analyzes multiple web sessions and determines its correlation between the sessions and web attack efficiently. Therefore, proposed system has advantages on extracting the latest web attack events efficiently by designing and implementing the multiple web session and log correlation analysis actively.

• Journal of Korea Society of Industrial Information Systems
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• v.11 no.2
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• pp.63-68
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• 2006

If web server occurs my fault, log file is important information to find out occurred error. The web server analyzer plays a vital role in improving the web service quality by analyzing log files stores in the web server. But, most conventional web server analysis programs are provided over wired networks, there are problems to be dealt with in terms of time-space restrictions and mobility. In order to solve above problem, this paper represent web server analyser based mobile. In case web server manager based mobile, it can immediately know recent log information accessing real-rime as also anytime, anywhere.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.88-93
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• 2005

Web usage mining is a research field for searching potentially useful and valuable information from web log file. Web log file is a simple list of pages that users refer. Therefore, it is not easy to analyze user's current interest field from web log file. This paper presents web usage mining method for finding users' current interest based on fuzzy categories. We consider not only how many times a user visits pages but also when he visits. We describe a user's current interest with a fuzzy interest degree to categories. Based on fuzzy categories and fuzzy interest degrees, we also propose a method to cluster users according to their interests for user modeling. For user clustering, we define a category vector space. Experiments show that our method properly reflects the time factor of users' web visiting as well as the users' visit number.

• Journal of Information Technology and Architecture
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• v.11 no.1
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• pp.25-32
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• 2014

Web mining techniques are often used to discover useful patterns from data log generated by Web servers for the purpose of web usage analysis. Yet traditional Web mining techniques do not reflect sufficiently sequential properties of Web log data. To address such weakness, we introduce a framework for analyzing Web access log data by using process mining techniques. To illustrate the proposed framework, we show the analysis of Web access log in a campus information system based on the framework and discuss the implication of the analysis result.

• Journal of Information Processing Systems
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• v.7 no.4
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• pp.707-716
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• 2011

It is important that desktop grids should be able to aggressively deal with the dynamic properties that arise from the volatility and heterogeneity of resources. Therefore, it is required that task scheduling be able to positively consider the execution behavior that is characterized by an individual resource. In this paper, we implement a log analysis system with REST web services, which can analyze the execution behavior by utilizing the actual log data of desktop grid systems. To verify the log analysis system, we conducted simulations and showed that the resource group-based task scheduling, based on the analysis of the execution behavior, offers a faster turnaround time than the existing one even if few resources are used.

• Journal of the Korea Computer Industry Society
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• v.5 no.9
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• pp.1063-1068
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• 2004

A log file in web server stores various information of web server. The web server information is good reference information to improve web service quality. Also if web server occurs any fault, log file information is important information to find out occurred error. Currently, many of web server manager that they have wire have disadvantages in mobility. In order to solve above problem, this paper represent web server manager based mobile. Incase web server manager based mobile, it can immediately know recent log information accessing real-rime as also anytime, anywhere

• Journal of Korea Society of Digital Industry and Information Management
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• v.8 no.1
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• pp.17-27
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• 2012

Most of the current cultivation log is recorded by growers manually or whose environment information is input based on the grower's own judgement. It is not easy for growers to record the image of disease and insect pests in the cultivation log. In this regards, u-Farm cultivation log management system based on Web 2.0 and smart phone is introduced in this paper, that is used for the effective cultivation management and grower's convenience. In the system, the value of environment information is transmitted to servers via sensors. It designs and implements Web 2.0 Flex so as to make the cultivation log more reliable and provide users with active, convenient GUI in case the log is recorded by desktop. In this process, detailed modules are designed by object-oriented UML considering the reusability and maintenance of the system. Moreover, the system introduced in this paper is proved to be more excellent by the qualitative assessment compared with the other researches.

• Information Systems Review
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• v.4 no.1
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• pp.47-67
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• 2002

Since mid 90's, most of firms utilizing web as a communication vehicle with customers are keenly interested in web log file which contains a lot of trails customers left on the web, such as IP address, reference address, cookie file, duration time, etc. Therefore, an appropriate analysis of the web log file leads to understanding customer's behaviors on the web. Its analysis results can be used as an effective marketing information for locating potential target customers. In this study, we introduced a web mining technique using Clementine of SPSS, and analyzed a set of real web log data file on a certain Internet hub site. We also suggested a process of various strategies build-up based on the web mining results.