Prof. Prajkta Khaire, Rohit Narawade, Vaibhav Badgujar et al. · International Journal for Research in Applied Science and Engineering Technology · 2022
Abstract: This is a design and implementation of a Web Server. “WEB SERVER ANALYSIS” is a software developed for managing log files on the server. Web log analysis is an innovative and unique field constantly formed and changed by the convergence of various emerging Web technologies. Due to its interdisciplinary character, the diversity of issues it addresses, and the variety and number of Web applications, it is the subject of many distinctive and diverse research methodologies. This project deals with the analysis of the log files of a web server. Keywords: Web server, Log files, Error, URL, Resources
Cited by 0Open access
Noriko Hanakawa, Masaki Obana · International Journal of Software Engineering & Applications · 2018
We propose a log-based analysis tool for evaluating web application computer system. A feature of the tool is an integration software log with infrastructure log. Software engineers alone can resolve system faults in the tool, even if the faults are complicated by both software problems and infrastructure problems. The tool consists of 5 steps: preparation software, preparation infrastructure, collecting logs, replaying the log data, and tracing the log data. The tool was applied to a simple web application system in a small-scale local area network. We confirmed usefulness of the tool when a software engineer detects faults of the system failures such as "404" and "no response" errors. In addition, the tool was partially applied to a real large-scale computer system with many web applications and large network environment. Using the replaying and the tracing in the tool, we found causes of a real authentication error. The causes were combined an infrastructure problem with a software problem. Even if the failure is caused by not only a software problem but also an infrastructure problem, we confirmed that software engineers alone could distinguish between a software problem and an infrastructure problem using the tool.
Cited by 2Open access
S. E. Kruck, Faye P. Teer, William A. Christian · Industrial Management & Data Systems · 2008
Purpose The purpose of this paper is to describe a new software tool that graphically depicts analysis of visitor traffic. This new tool is the graph‐based server log analysis program (GSLAP). Design/methodology/approach Discovering hidden and meaningful information about web users' patterns of usage is critical to optimization of the web server. The authors designed and developed GSLAP. Presented in this paper is an example of GSLAP in the context of an analysis of the web site of a small fictitious company. Also included is an explanation of current literature that supports graphical display of data as a cognitive aid to understanding data. Findings GSLAP is shown to provide a visual server log analysis that is a great improvement on the textual server log. Research limitations/implications The benefits of the output from GSLAP are compared with the typical textual output. Originality/value The paper describes a software tool that helps the analysis of usage patterns of web traffic.
Cited by 6
Newspaper Paulo Batista, Paulo Batista, Mário J. Silva et al. · 2007
With the explosive growth of data available on the Internet, personalization of this information space become a necessity. An important component of web personalization is the automatic knowledge extraction from web log files. However, analysis of large web log files is a complex task not fully addressed by existing web access analyzers. Using commercial software, we applied well-known data mining techniques (association rules and clustering) to analyze access log records collected on a web newspaper. This paper identifies several reading patterns and discusses approaches for mining this data.
Cited by 0
Paulo Batista, Mário J. Silva · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2002
With the explosive growth of data available on the Internet, personalization of this information space become a necessity. An important component of web personalization is the automatic knowledge extraction from web log files. However, analysis of large web log files is a complex task not fully addressed by existing web access analyzers. Using commercial software, we applied well-known data mining techniques (association rules and clustering) to analyze access log records collected on a web newspaper. This paper identifies several reading patterns and discusses approaches for mining this data.
Cited by 31Open access
N. Warren · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2019
Much of the literature on web log analysis focuses on how to collect summary statistics, the anatomy of web logs, how to choose analysis software, and the strengths and weaknesses of log analysis. These issues are important to understand, but less research has been done on how to comprehensively apply web logs to higher-level issues within libraries. This study shows that web statistics are a tool to help librarians make management, marketing, and collection development decisions. This paper will cover the basic issues of web log analysis in addition to exploring practical application concepts for the library of the National Institute of Environmental Health Sciences (NIEHS). The library's web logs were analyzed from January to March 2002. In addition, a database was queried to track usage of specific resources on the library's website from March to May 2002.
Cited by 3Open access
Santiago del Rey, Silverio Martínez‐Fernández, Antonio Salmerón · IET Software · 2023
Abstract Software organisations aim to develop and maintain high‐quality software systems. Due to large amounts of behaviour data available, software organisations can conduct data‐driven software maintenance. Indeed, software quality assurance and improvement programs have attracted many researchers' attention. Bayesian Networks (BNs) are proposed as a log analysis technique to discover poor performance indicators in a system and to explore usage patterns that usually require temporal analysis. For this, an action research study is designed and conducted to improve the software quality and the user experience of a web application using BNs as a technique to analyse software logs. To this aim, three models with BNs are created. As a result, multiple enhancement points have been identified within the application ranging from performance issues and errors to recurring user usage patterns. These enhancement points enable the creation of cards in the Scrum process of the web application, contributing to its data‐driven software maintenance. Finally, the authors consider that BNs within quality‐aware and data‐driven software maintenance have great potential as a software log analysis technique and encourage the community to deepen its possible applications. For this, the applied methodology and a replication package are shared.
Cited by 8Open access
Shunhua Tan, Miao Chen, Guohai Yang · 2010
In this paper we propose a web log mining-based network user behavior analysis scheme, which plays an important role in network structure optimization and website server configuration. Based on clustering and regression model, we studied the network user's visit model in a university by analyzing a large amount of web log data which is collected from the university campus network. The data analyzing software was developed by VC and SQL Server 2000.
Cited by 8
Hamed Jelodar · 2014
Requests that users are sent to the Web server all in server log files are stored in a file called. It can be stated that users will see all pages or all user requests to the server, the file is stored. In this paper we investigate the behavior of users through web usage mining techniques and using server log files to help service administrators paid websites. At the end a web site downloading software analysis is carried out and the results are shown in the drawing.
Cited by 0
Shiva Asadianfam · Zenodo (CERN European Organization for Nuclear Research) · 2014
RapidMiner is a software for machine learning, data mining, predictive analytics, and business analytics. The server will record large web log files when user visits the website. Extracting knowledge from such huge data demands for new methods. In this paper, we propose a web usage mining method with RapidMiner. At first, the redundant files in log file are deleted by Matlab and then we mines web log which has been pretreated with RapidMiner, it obtains the custom of different user to visit the website by processing and analyzing log file, and mines unusual rules, and provides the reference for the policy decision and construction of website. Experimental result analysis show that, applying RapidMiner in web usage mining, will obtain frequent model which user visits the website, manage to optimize the website structure and recommends for users.
Cited by 6Open access
Constantine J. Aivalis, Anthony C. Boucouvalas · 2011
This paper describes the implications of the new trends in web development languages on log file analysis for e-commerce. The new trends in Software Development and the available Web Analytics technologies are explained. The focus is placed on the diversifications introduced to the traces left on the system that by Rich Internet Applications (RIAs). Finally a novel hybrid solution is proposed that is based on the junction of log files with operational data and page tagging, which allows even exacter measurements of customer behavior. It allows a customization of the Analysis Tool and survives the shift of the technologies.
Cited by 12
Šimunić, Mislav, Hutinski, Željko, Čubrilo, Mirko · University of Zagreb University Computing Centre (SRCE) · 2005
Demand markets dictate and pose increasingly more requirements to the supply market that are not easily satisfied.The supply market presenting its web pages to the demand market should find the best and quickest ways to respond promptly to the changes dictated by the demand market.The question is how to do that in the most efficient and quickest way.The data on the usage of web pages on a specific web site are recorded in a log file.The data in a log file are stochastic and unordered and require systematic monitoring, categorization, analyses, and weighing.From the data processed in this way, it is necessary to single out and sort the data by their importance that would be a basis for a continuous generation of dynamics/changes to the web site pages in line with the criterion chosen.To perform those tasks successfully, a new software solution is required.For that purpose, the authors have developed the first version of the WLE (WebLogExplorer) software solution, which is actually realization of web page multidimensionality and the web site as a whole.The WebLogExplorer enables statistical and semantic analysis of a log file and on the basis thereof, multidimensional control of the web page dynamics.The experimental part of the work was done within the web site of HTZ (Croatian National Tourist Board) being the main portal of the global tourist supply in the Republic of Croatia (on average, daily "log" consists of c. 600,000 sets, average size of log file is 127 Mb, and c. 7000-8000 daily visitors on the web site).
Cited by 0Open access
North American Academic Research · Zenodo (CERN European Organization for Nuclear Research) · 2025
In today’s era dominated by software applications and smart systems, our daily activities increasingly rely on various smart applications, including shopping apps, e-commerce, web, and social media applications. As these applications and systems grow, so does the challenge of monitoring and maintaining their 24/7 operation, posing a significant task for online service providers. Logs, key tools for recording system runtime information, are crucial in managing web services. However, as systems and applications become more complex, manual review of log records becomes time-consuming and impractical. The development of automated log analysis tools has recently garnered significant attention from researchers in the academic and industrial sectors. These tools are pivotal in several downstream tasks, such as anomaly detection, failure prediction, and system diagnosis. The primary step in log analysis is log parsing, which involves transforming unstructured log messages into structured data for subsequent mining tasks. To date, over 30 log parsing tools have been developed. This paper focuses on an empirical study of fifteen log parsing tools, chosen for their public availability of source code and proven high accuracy and efficiency in prior research. The study was conducted using seven real datasets collected from servers at the National Agency for Network Services (NANS) at the Ministry of Communications and Technology in Syria, including an Apache web server, Linux Mail server, WHMCS (Web Hosting Billing & Automation Platform), Microsoft web server IIS (Microsoft Internet Information Services 10.0), Plesk (Web Hosting Control Panel), and a Cisco ASA 5512 Firewall device. A comparative analysis of these tools in terms of accuracy, efficiency, and scalability was performed to assist system administrators in selecting the most suitable log-parsing tool for their analysis tasks. The study finds that Drain demonstrates the best performance in these aspects. Our contributions provide a strong link between research and industry log parsing, consolidating past research efforts and facilitating future advancements.
Cited by 0Open access
Amit Dipch, Ji Kasliwal, Dr. Girish et al. · 2015
Abstract — In current trend of computer science, there are lots of software available for data mining, predictive analytics, and business analytics. In every sort of software where expectation comes for prediction then there is need for a web log files of user visit to particular website stored at server side. The interesting information for extracting knowledge from such huge data demands for new logic and new methods. In this paper work, we proposed a web usage mining method with a well-known mining tool software. At first, we are having a log file from KDD repository then we performed deletion on the redundant information from file and then we mining log file which has been submitted with mining tool, then going to get the customized form of access done by different user to visit the website by performing processing and analyzing phase on log file, and mine for so called unusual rules, and suggesting rules the as reference for the decision making and construction of website. At the end, we collected experimental result and analysis on result show that, applying web usage mining, will can look of frequent model which user visits the website, manage to optimize the website structure and recommends for users.
Cited by 0
Ortiz Ancona, Dante, Ballesteros Estrada, Silvia, Gama Mora, Mario et al. · E-LIS Repository (University of Naples Federico II) · 2012
Purpose: Presents a software system statistics generator, with configurable reports for decision making in the digital collections development, through the processing of log files generated by an EZproxy server.\nDesign / methodology / approach: This case study was written after implementing the software GESAR into the DGB-UNAM remote access service. The followed procedure was: process the log files generated by an EZproxy server and store the resulted information in three relationships within a relational database containing institutional catalogs (electronic resources providers, users, communities, campi, among others). In the present case, 159 statistical reports on the use of electronic resources were defined, with the option to define a larger amount. Then, through a graphical interface statistical reports were displayed in HTML and Microsoft Excel format, what makes possible to filter the information in different ways, making the number of displayed reports virtually uncountable.\nFindings: Although GESAR was designed for an EZproxy server, it can easily be adapted to any website. By analyzing statistical reports, it was found what kind of users accessed what resources, on what date and time, as well as the physical locations from where access was performed. This analysis allowed us to discover certain anomalies in the use of electronic resources by some users.\nOriginality / value: GESAR incorporates two new features not provided by any other similar tool: a) it generates reports based on users’ needs. b) utilization of a database with institutional catalogs to generate reports. The usage data allow to visualize who, where, when and what resources were consulted. The analyzed data universe was 263 million access records and 171,000 users.\nKeywords / topics: web analytics, transaction log analysis, web log analysis, log file analysis, log processing, statistics web sites, log analysis, collections management, library users, online databases.\nPublication Type: Case Study
Cited by 0Open access
Natthakorn Chuaychoo, Thitaporn Rimdusit, Chanankorn Jandaeng · NRCT Data Center · 2016
The network monitoring system needs all data sources in order to monitors network service and system, resource capacity plan, statistics and accounting, fault management and performance; such as throughput, latency and round trip time. SNMP, audit log and network traffic are required for data analysis together. This paper present software architecture for network monitoring system that consists of data collection, data analysis and data visualization. The software architecture is implemented in web based application to evaluate this architecture.
Cited by 1Open access
Harry Hochheiser, Ben Shneiderman, Harry Hochheiser et al. · Digital Repository at the University of Maryland (University of Maryland College Park) · 1999
HTTP server log files provide Web site operators with substantial detail regarding the visitors to their sites. Interest in interpreting this data has spawned an active market for software packages that summarize and analyze this data, providing histograms, pie graphs, and other charts summarizing usage patterns. While useful, these summaries obscure useful information and restrict users to passive interpretation of static displays. Interactive starfield visualizations can be used to provide users with greater abilities to interpret and explore web log data. By combining two-dimensional displays of thousands of individual access requests, color and size coding for additional attributes, and facilities for zooming and filtering, these visualizations provide capabilities for examining data that exceed those of traditional web log analysis tools. We introduce a series of interactive starfield visualizations, which can be used to explore server data across various dimensions. Possible use...
Cited by 0Open access
David, John · Digital Commons - DU (University of Denver) · 2024
The increased amount of web applications and internet software solutions utilizing cloud frameworks has contributed to large data sets of system log messages being generated constantly. These messages may contain sensitive data, creating an additional security risk for the systems and contributing to the need for analysis of such large volumes of data in real time. Large commercial data monitoring systems can solve for these analysis requirements, but they can be costly. We present a solution to analyzing web application log data which ingests it, processes it and visualizes sensitive data found within in real time. Our solution utilizes an open source method for bulk data analysis and extraction of sensitive information, a digital forensics utility called Bulk Extractor. We focus specifically on Personally Identifiable Information (PII) artifacts present within log data as the targets for extraction. We call our solution the PII Scanner, and present prototype implementations of the scanner on the Amazon Web Services cloud platform, as well as results from tests performed on them to demonstrate their effectiveness and explore implementation options.
Cited by 0
Uzama Tabaassum, Shreesampada, Lakshmi A et al. · Iconic Research and Engineering Journals · 2026
The rapid growth of software systems and internet-based applications has increased the risk of cyber threats such as phishing attacks, unauthorized access, and malicious software execution. Traditional security mechanisms are often insufficient to detect dynamic threats in real time. This paper presents a *Security Assessment Model for Software System*, which integrates system monitoring, phishing detection, executable file analysis, and authentication log analysis into a unified web-based platform. The proposed system continuously monitors system processes, network activity, and resource usage while allowing users to analyze website URLs and uploaded executable files for potential threats. Authentication log analysis is used to detect suspicious login activities such as repeated failed attempts or unauthorized access. The system is developed using Python Flask for backend processing, SQLite for data storage, and machine learning–based decision logic for phishing detection. Experimental evaluation demonstrates that the system effectively identifies suspicious behavior and provides timely security alerts. The proposed model can assist system administrators in enhancing software system security by proactively detecting and mitigating threats.
Cited by 0Open access
Resul Daş, İbrahim Türkoğlu, MEHMET Poyraz · DergiPark (Istanbul University) · 2008
Web usage mining is to analysis Web log files to discover user accessing patterns of Web pages. The web server access logs records very significant information about websites. Information about a web site such as links between the pages, the users' profile, and demographic properties can be obtained from the web server access logs. In this study, user access logs of the Web server of Firat University were analyzed with Web mining software by using Web usage mining method. The end of analysis, it was found useful information about general statistics like most requested pages, file access, entry pages access, file type access, file extension access within the visited pages etc. The outcomes of the study will be used in order to development of the website to increase its effectiveness.
Cited by 2Open access