DOCSLIB.ORG

Free Web Access Log Analyzer

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Free Web Access Log Analyzer Free web access log analyzer click here to download Deep Log Analyzer is the best free Web analytics software I've found. It is a local log analysis tool that works on your site logs without requiring. WebLog Expert is a powerful log analyzer with a free day trial version available. Supports IIS, Apache and Nginx logs. Yes. Yes Built-in web server. Yes. WebLog Expert is a powerful access log analyzer. It can give you WebLog Expert can analyze logs of Apache, IIS and Nginx web servers. It can even read GZ. AWStats is a free powerful and featureful tool that generates advanced web, it can work with all web hosting providers which allow Perl, CGI and log access.​What is AWStats] · ​Features] · ​Downloads]. GoAccess is an open source real-time web log analyzer and interactive use your browser (great if you want to do a quick analysis of your access log via SSH. I am looking into using a free IIS log analyzer to get web metrics for our Although the name says apache it can even open IIS logs or W3C. Home of the Webalizer, a fast, free web server log analysis program. Handles standard Common logfile format (CLF) server logs, several. Apache Log Viewer (ALV) is a free tool which lets you monitor, view and analyze Apache or IIS logs with more ease. Open web server logs. With apache logs. Deep Log Analyzer is professional website statistics and web analytics software for analyzing IIS, Apache, Nginx and other web server logs. Reports on visitors, website and convert more visitors to satisfied customers. And it's FREE to try! Web servers powering millions of websites across the globe generate large amount of log data on daily basis. Often webmasters use these log. Free web log analysers/analyzers and web statistics programs for your log analyzer that generates graphical statistics from your web logs. Apache Logs Viewer (ALV) is a free and powerful tool which lets you monitor, view and analyze apache logs with more ease. It offers search. Web log analysis software is a kind of web analytics software that parses a server log file from a From Wikipedia, the free encyclopedia. Jump to: navigation. Web Log Storming is an interactive web server log file analyzer (IIS, Apache and Your web logs are kept in fast computer memory allowing you to view filtered. GoAccess is a free text/curses based log analyzer similar to "top". This might seem like a bit of overkill, but Splunk offers web log analyzing and much, much. WebLog Expert Lite is a powerful web server log analyzer. visitors: activity statistics, file access statistics, information about referring pages, . Free Web Log Analyzer software (J-Bot Finder) is designed to allow you to. The SHADOW Apache Access Log Analyzer Tool is used to parse the access log from a website running the Apache web server. W3Perl is a free logfile analyzer for web, ftp, squid, cups and mail server. Incremental mode, don't need to reload the whole log file. - Support for many servers. Popular Alternatives to Apache Logs Viewer for Windows, Linux, Mac, Web, Self-Hosted and The Webalizer is a fast, free web server log file analysis program. Web servers create detailed and verbose web logs in the form of text files. Web analyzers parse the details of those text files and perform analysis. Although this tool is not exactly free, there is a lite version of it, which is open source. Nihuo Web Log Analyzer is a fast and powerful web access log analyzer for small and free fully functional day trial version of Nihuo Web Log Analyzer. As more companies move to the cloud it is important to use logs to and web developers can all use logs to make better data-driven decisions. Note: These are in no particular order and include both free and paid tools. Visitors is a very fast web log analyzer for Linux, Windows, and other Visitors is free software (and of course, freeware), under the terms of the GPL license visitors -A -m 30 www.doorway.ru -o html -- trails --prefix www.doorway.ru > www.doorway.ru Download a free day trial of Log & Event Manager, an award-winning SIEM Collect IIS web server logs and identify threats with log parsing and real-time. Desktop-based freeware IIS log analyser (Apache logs is also supported). Tracking statistical Download this extension Get official downloads with the Web Platform Installer. Category, Manage. License, Free. Supported by. Project Description Indihiang Project is a web log analyzing tool. The tool analyzes IIS and Apache Web servers in comprehensive graphs and. The AlterWind Log Analyzer is web site log analysis and web statistics software. Our log file AlterWind Log Analyzer Lite is a free web analyzer tool. This version of Simultaneous analysis of a large number of web server logs. The log files. Analog shows you the usage patterns on your web server. It's. Ultra- Reports in 32 languages; Works on any operating system; Free software. The Sumo Logic App for IIS puts the power of real-time log analysis in your hands, Collect logs from all your IIS servers, custom applications, web application. Log Parser Lizard GUI, the powerful and versatile query software from Lizard Labs provides universal query access (using SQL) to text-based data, such as log files, Lizard as your query software,web log analyzer and system log analyzer. Download a free trial of Event Log Analyzer - Event Log management and EventLog Analyzer can manage logs from distributed environment as well. LOGalyze has become an open source log management tool and it is free for LOGalyze collects event logs from distributed Windows hosts or syslogs from. Web Log Analysis is an automatic system that analyze all web logs traffic. Download Free Now & Get Free Support Sessions With Every. GoAccess is an open source web log analyzer. You can use it for analysis of logs on a real-time basis in. Analyzing logs can be fun, tricky, frustrating and valuable – all at the In this article, let me show you three somewhat less popular log analysis tools. Next post: How to get your own free Amazon Web Services server in. BBClone - A PHP web counter that logs and show each IP address with The Big Brother Log Analyzer (BBLA) - A free open-source image based page tracker. The Webalizer is a fast, free web server log file analysis program. wu-ftpd/proftpd xferlog (FTP) format logs, Squid proxy server native format. Here is the www.doorway.ru guide to using the ELK Stack as an Apache log analyzer. Why is Apache so popular (as shown in that statistic)? It's free and open to the web logs server every second, extracting actionable data from thousands of log. The SmarterStats website analyzer can help protect against 20+ common issues Data mining takes a "deep-dive" into website statistics, and web server logs, The Free Edition of SmarterStats is perfect for individuals looking to optimize. Deep Log Analyzer is the best free Web analytics software I've found. It is a local log analysis tool that works on your site logs without requiring. AWStats is a free. S3stat is a service that takes the detailed server access logs provided by Amazon's CloudFront and Simple Storage Service (S3), and translates. Sample Log Reports Log Reports SafeSquid Logs are produced in three distinct formats. The Webalizer is a fast, free web server log file analysis program. It's undoubtedly one of the most powerful, free Web analytics software. Unlike most of the other log analysis tools, Deep Log Analyzer logs. The world's most popular cloud-based, enterprise-class log management service, Try Loggly for free Real-time analysis of your logs and machine data. See downloads page for more free Splunk offerings from Syslog, Windows events logs, or WC3 logs generated by web application servers, GFI provides a comprehensive log data analysis platform for Security Information. Deep Log Analyzer. Deep Log Analyzer is the best free Web analytics software I've found. It is a local log analysis tool that works. on your site logs without. Open Web Analytics (OWA) is a free and open source web analytics software that Webalizer is a fast, free web server log analysis program. GoAccess is a real-time web log analyzer and interactive viewer that runs in a . No configuration needed You can just run it against your access log file, pick the log .. Feel free to use the Github issue tracker and pull requests to discuss and. Searching detailed web site statistics software and a good log analysis tool? Try free trial log analyzer download here. and inexpensive way to sift through gigabytes of logs and produce easy-to-understand summaries of your web site traffic. GoAccess is an interactive and real time web server log analyzer program that quickly analyze and view web server logs. Download Your Free eBooks NOW - 10 Free Linux eBooks for Administrators | 4 Free Shell Scripting. software for Windows. Web log analyzer, WMS log analyzer, proxy log analyzer. Explore your site, proxy or other logs without any limits! The Web Log. Log file analysis helps you get % accurate data about how Googlebot is crawling your website. You can do it for free and access actionable insights. If your web server is powered by Apache or NGinx, make sure the. www.doorway.ru: Sawmill is a universal log analysis/reporting tool for almost any log including web, media, email, security, network and application logs. Webalizer is a fantastic, free web server log file analyzer. It is fast and generates very nice statistical reports about who is hitting your site and what they are.
Load more

Recommended publications
  • Application Log Analysis
    Masarykova univerzita Fakulta}w¡¢£¤¥¦§¨ informatiky !"#$%&'()+,-./012345<yA| Application Log Analysis Master’s thesis Júlia Murínová Brno, 2015 Declaration Hereby I declare, that this paper is my original authorial work, which I have worked out by my own. All sources, references and literature used or excerpted during elaboration of this work are properly cited and listed in complete reference to the due source. Júlia Murínová Advisor: doc. RNDr. Vlastislav Dohnal, Ph.D. iii Acknowledgement I would like to express my gratitude to doc. RNDr. Vlastislav Dohnal, Ph.D. for his guidance and help during work on this thesis. Furthermore I would like to thank my parents, friends and family for their continuous support. My thanks also belongs to my boyfriend for all his assistance and help. v Abstract The goal of this thesis is to introduce the log analysis area in general, compare available systems for web log analysis, choose an appropriate solution for sample data and implement the proposed solution. Thesis contains overview of monitoring and log analysis, specifics of application log analysis and log file formats definitions. Various available systems for log analysis both proprietary and open-source are compared and categorized with overview comparison tables of supported functionality. Based on the comparison and requirements analysis appropriate solution for sample data is chosen. The ELK stack (Elasticsearch, Logstash and Kibana) and ElastAlert framework are deployed and configured for analysis of sample application log data. Logstash configuration is adjusted for collecting, parsing and processing sample data input supporting reading from file as well as online socket logs collection. Additional information for anomaly detection is computed and added to log records in Logstash processing.
    [Show full text]
  • Analysis of Web Logs and Web User in Web Mining
    International Journal of Network Security & Its Applications (IJNSA), Vol.3, No.1, January 2011 ANALYSIS OF WEB LOGS AND WEB USER IN WEB MINING L.K. Joshila Grace 1, V.Maheswari 2, Dhinaharan Nagamalai 3, 1Research Scholar, Department of Computer Science and Engineering [email protected] 2 Professor and Head,Department of Computer Applications 1,2 Sathyabama University,Chennai,India 3Wireilla Net Solutions PTY Ltd, Australia ABSTRACT Log files contain information about User Name, IP Address, Time Stamp, Access Request, number of Bytes Transferred, Result Status, URL that Referred and User Agent. The log files are maintained by the web servers. By analysing these log files gives a neat idea about the user. This paper gives a detailed discussion about these log files, their formats, their creation, access procedures, their uses, various algorithms used and the additional parameters that can be used in the log files which in turn gives way to an effective mining. It also provides the idea of creating an extended log file and learning the user behaviour. KEYWORDS Web Log file, Web usage mining, Web servers, Log data, Log Level directive. 1. INTRODUCTION Log files are files that list the actions that have been occurred. These log files reside in the web server. Computers that deliver the web pages are called as web servers. The Web server stores all of the files necessary to display the Web pages on the users computer. All the individual web pages combines together to form the completeness of a Web site. Images/graphic files and any scripts that make dynamic elements of the site function.
    [Show full text]
  • System Log Files Kernel Ring Buffer Viewing Log Files the Log Files
    System Log Files Most log files are found in /var/log Checking logs are critical to see if things are working correctly Checking logs is critical to see if things are working correctly. Take a look at all the log files on scratch ls /var/log Kernel Ring Buffer The kernel ring buffer is something like a log file for the kernel; however, unlike other log files, it’s stored in memory rather than in a disk file. You can use the dmesg command to view it. Many times it is logged to /var/log/dmesg as well. It requires sudo privileges to read the /var/log/dmesg file, but not to run the dmesg command. Viewing log files There are a number of commands to view log files. cat less head tail Anytime a new entry is added to a log file, it is appended to the end of the file. This is one of those times where tail is particularly useful. Usually when we want to look at log files we want to look at the most recent entries. When organizing our viewing command - order matters. Most of the following commands produce different results. And all are useful depending on what type of results you want. Go through the thought process and figure out what each command does. Can you figure out which three produce identical results? cat /var/log/syslog cat /var/log/syslog | grep daemon cat /var/log/syslog | grep daemon | tail -n 10 cat /var/log/syslog | tail -n 10 cat /var/log/syslog | tail -n 10 | grep daemon less /var/log/syslog less /var/log/syslog | tail -n 10 | grep daemon head -n 10 /var/log/syslog head -n 10 /var/log/syslog | grep daemon tail -n 10 /var/log/syslog tail -n 10 /var/log/syslog | grep daemon If you add the -f option to the tail command it provides a live watch of the log file.
    [Show full text]
  • Linux and Open Source for (Almost) Zero Cost PCI Compliance
    Linux and Open Source for (Almost) Zero Cost PCI Compliance Rafeeq Rehman 2 Some Introductory Notes ¡ Payment Card Industry (PCI) standard is not a government regulaon. ¡ Who needs to comply with PCI? ¡ Twelve major requirements covering policy, processes, and technology to protect Credit Card Data. ¡ What is Credit Card Data? ¡ Few Clarificaons ¡ Payment Card Industry (PCI) requires some tasks to be performed by external vendors depending upon merchant level. There is no other way around, unfortunately. ¡ Open Source soluCons do need people. That is why it is almost free but not totally free. 9/10/11 3 What the Auditors Look For? ¡ Is PCI just a checklist? ¡ Are auditors genuinely interested in securing the PCI data? ¡ Does it maer if you use an open source or commercial product to meet PCI requirements? ¡ What if you meet PCI requirements while improving security and spending less money? 9/10/11 4 Is it viable to use Open Source for PCI Compliance? ¡ Is there a real company who uses Open Source soQware to achieve PCI compliance? Is it even possible? ¡ PCI 2.0 focuses more on Risk based approach. ¡ PCI (or any compliance) is boring! Make it interesCng by using Open Source. 9/10/11 5 PCI Biggest Expenses 1. Log Management (Storage and archiving, Monitoring and Alerng) 2. Vulnerability Scanning 3. Network Firewalls and Network Segmentaon 4. Intrusion DetecCon System 5. EncrypCon for data-at-rest 6. File Integrity Monitoring 7. IdenCty Management (Password controls, Two factor for remote access, Role based access) 9/10/11 6 AddiConal PCI
    [Show full text]
  • Forensic Investigation of P2P Cloud Storage Services and Backbone For
    Forensic investigation of P2P cloud storage services and backbone for IoT networks : BitTorrent Sync as a case study Yee Yang, T, Dehghantanha, A, Choo, R and Yang, LK http://dx.doi.org/10.1016/j.compeleceng.2016.08.020 Title Forensic investigation of P2P cloud storage services and backbone for IoT networks : BitTorrent Sync as a case study Authors Yee Yang, T, Dehghantanha, A, Choo, R and Yang, LK Type Article URL This version is available at: http://usir.salford.ac.uk/id/eprint/40497/ Published Date 2016 USIR is a digital collection of the research output of the University of Salford. Where copyright permits, full text material held in the repository is made freely available online and can be read, downloaded and copied for non-commercial private study or research purposes. Please check the manuscript for any further copyright restrictions. For more information, including our policy and submission procedure, please contact the Repository Team at: [email protected]. Note: This is authors accepted copy – for final article please refer to International Journal of Computers & Electrical Engineering Forensic Investigation of P2P Cloud Storage: BitTorrent Sync as a Case Study 1 2 3 1 Teing Yee Yang , Ali Dehghantanha , Kim-Kwang Raymond Choo , Zaiton Muda 1 Department of Computer Science, Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, UPM Serdang, Selangor, Malaysia 2 The School of Computing, Science & Engineering, Newton Building, University of Salford, Salford, Greater Manchester, United Kingdom 3 Information Assurance Research Group, University of South Australia, Adelaide, South Australia, Australia. Abstract Cloud computing has been regarded as the technology enabler for the Internet of Things (IoT).
    [Show full text]
  • The Migration Process of Mobile Agents Implementation, Classification, and Optimization
    The Migration Process of Mobile Agents Implementation, Classification, and Optimization Dissertation zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.), vorgelegt dem Rat der Fakult¨atf¨ur Mathematik und Informatik der Friedrich-Schiller-Universit¨atJena von Diplom-Informatiker Peter Braun, geboren am 22. Juni 1970 in Neuss. Gutachter 1. Prof. Dr. Wilhelm R. Rossak, Friedrich-Schiller-Universit¨atJena 2. Dr. Bill Buchanan, Napier University, Edinburgh, Scotland Tag der letzten Pr¨ufung des Rigorosums: 30. April 2003 Tag der ¨offentlichen Verteidigung: 13. Mai 2003 Abstract Mobile agents provide a new and fascinating design paradigm for the architecture and programming of distributed systems. A mobile agent is a software entity that is launched by its owner with a user-given task at a specific network node. It can decide to migrate to other nodes in the network during runtime. For a migration the agent carries its current data, its program code, and its execution state with it. Therefore, it is possible to continue agent execution at the destination platform exactly where it was interrupted before. The reason for a migration is mainly to use resources that are only available at remote servers in the network. This thesis focuses on the migration process of mobile agents, which is to our knowl- edge not considered in literature so far, although the performance of a mobile agent based application strongly depends on the performance of the migration process. We propose a general framework an an innovative set of notions to describe and specify the migration process. By introducing the concept of a migration model, we offer a classification scheme to describe migration issues in existing mobile agent systems.
    [Show full text]
  • Log File Management Tool Deployment and User's Guide
    Log File Management Tool Deployment and User's Guide Genesys Care/Support current 9/9/2021 Table of Contents Log File Management Tool Deployment and User's Guide 8.5.104 4 Overview 5 Architecture 8 New in this Release 11 Downloading LFMT 13 Known Issues and Limitations 14 Migration to LFMT 8.5.104 15 Log File Management Tool Deployment Planning 17 LFMT Client - GAX Dependencies 18 LFMT Database Sizing 19 LFMT Storage and Resource Sizing 21 Log File Management Tool General Deployment 24 Prerequisites 25 Deployment of the LFMT Client 27 Deployment of the LFMT Indexer 29 Deployment of the LFMT Collector 32 Configuration of the LFMT Database 36 Initializing the DBMS 40 Deployment of Workbench Agent for LFMT 8.5.1 44 Installing Workbench Agent (Mass Deployment) for LFMT 8.5.1 49 LFMT Application Connections 59 Log File Management Tool Configuration Options 60 LFMT Host Object Configuration Options 61 LFMT GAX Configuration Options 62 LFMT Indexer Configuration Options 64 LFMT Collector Configuration Options 67 LFMT DAP Object Configuration Options 73 Workbench Agent Configuration Options (for LFMT 8.5.1) 74 Workbench Agent Host Object Configuration Options (for LFMT 8.5.102) 78 Log File Management Tool User's Guide 79 Configuration of Access Control for LFMT Users 80 Site Configuration 85 Collection Schedules 89 Force Collection 93 Indexing and Scrubbing 97 Log File Packaging 101 Available Packages 104 LFMT Audit Information 108 Additional Information 111 Configuration of TLS Connections 112 Best Practices 116 Regular Expressions 117 Release Notes 119 Log File Management Tool Deployment and User's Guide 8.5.104 Log File Management Tool Deployment and User's Guide 8.5.104 The Log File Management Tool (LFMT) is an intelligent, configurable, centralized log collection tool developed by Genesys Customer Care.
    [Show full text]
  • Dude, Where's My Log File?
    Dude, Where’s My Log File? Making the Most of Progress OpenEdge Log Files Michael Banks Principal Software Engineer, OpenEdge Progress Software Introduction Why Log Files? • History • Troubleshooting • Security Introduction . Many components . Diverse technologies . Some common • AppServer, WebSpeed, Pacific AppServer for OpenEdge • Servlet container hosted components (adapters mainly) • ubroker.properties configuration 3 © 2014 Progress Software Corporation. All rights reserved. Goals for This Session How can I Where control the What is in should I type and the log file? look? amount of messages? 4 © 2014 Progress Software Corporation. All rights reserved. Progress OpenEdge Database Database Log <dbdir>/<dbname>.lg . Location cannot be changed . Startup parameter settings . Date/time startup and shutdown . User login/logout . System error messages . Utility and maintenance activity . SQL server startup/shutdown 5 © 2014 Progress Software Corporation. All rights reserved. Progress OpenEdge Database – Log Format [2014/09/16@15:25:45.022-0400] P-13259 T-47842003425760 I DBUTIL : (451) p t t t i b i f b k bth [ [2014/09/16@15:25:4 P- 13259 T- 47842003425760 ER 0: (333) Multi-user session begin. [2014/09/16@15:25:45.509-0400] P-13267 T-469809696I BROKER 0 : (15321) Before Image Log Initialization at block 0 offset 0. [2014/09/16@15:25:45.622-0400] P-13267 T-46980969695680 I BROKER 0: (452) Login by mbanks on batch. [ 6980969695680 I BROKER 0: (4234) P(452) g Loginp E g by mbanksa . Lon batch. u 61 on Linux devlinux15 2.6.18-164.el5 #1 SMP Tue Aug 18 15:51:48 EDT 2009 x86_64.
    [Show full text]
  • Forensics in Peer-To-Peer Sharing and Associated Litigation Challenges
    Forensics in Peer-to-Peer Sharing and Associated Litigation Challenges Presented by Mo Hamoudi, Seattle, WA ([email protected]) Terry Lahman, Snoqualmie, WA ([email protected]) 1 Statement of Probable Cause “Between April 08, 2016, and April 09, 2016, while acting in an undercover capacity, I used a law enforcement version of eMule, a commonly used P2P file sharing program for the eD2k file sharing network, to monitor for P2P users possessing and distributing image and video files depicting child pornography. I used the law enforcement version of eMule to download several files depicting child pornography from a P2P user at IP address <redacted> (the SUBJECT IP ADDRESS).” The statement of probable cause implies the detective was sitting at a computer utilizing a special version of software to identify and download suspected child pornography. It also implies that the specialize software is running autonomously on the detective’s computer. The law enforcement version of eMule runs automatically without user invention. And the law enforcement computer is only one component of a large scale network of computers. Digging into the technical data of the law enforcement version of eMule required a deep investigation similar to peeling an onion, one layer at a time. The entire process required a number of discovery requests to peel back each layer. Each new discovery item was analyzed to dig deeper into the next layer. PRACTICE POINT: The practitioner needs to use the Federal Rules of Criminal Procedure 16 or its state counterpart to obtain information beyond the affidavit. United States v. Soto-Zuniga, 837 F.3d 992 (9th Cir.
    [Show full text]
  • Bluetooth Networking for Smartcards
    Departement Elektrotechnik Professur für Technische Informatik Professor Dr. Albert Kündig Alain Pellmont Andreas Petralia Bluetooth Networking for Smartcards Diploma Thesis WS-2001.05 Winter 2000=2001 Supervisors: Prof. Dr. Albert Kundig¨ Dr. George Fankhauser Bernard Stauffer Public Release Institut für Technische Informatik und Kommunikationsnetze Computer Engineering and Networks Laboratory ii Supervisors: Prof. Dr. Albert Kundig,¨ [email protected] Dr. George Fankhauser, [email protected] Bernard Stauffer, stauff[email protected] Students: Alain Pellmont, [email protected] Andreas Petralia, [email protected] Acknowledgments Our special appreciation goes to our supervisors Prof. Dr. Albert Kundig,¨ Bernard Stauffer and Dr. George Fankhauser for entrusting us with this project. Prof. Dr. Albert Kundig¨ and Bernard Stauffer work for the Com- puter Engineering and Networks Laboratory [49] at the Swiss Federal Insti- tute of Technology Zurich [48]. Dr. George Fankhauser works for acter ag [3] where the authors were enabled to work in a stimulating atmosphere and wonderful environment. Furthermore, we thank acter ag [3] and their staff for their support, AXIS [11] for the Bluetooth stack and Lesley Brack, Emmanuelle Graf, Kathy Grolimund and Ian Maloney for their proof-reading and comments. Remaining mistakes are ours. Zurich, 17th March 2001 Alain Pellmont Andreas Petralia iii iv Aufgabenstellung Bluetooth Networking for Smartcards Alain Pellmont und Andreas Petralia Diplomarbeit TIK-DA-2001.05 Winter 2000/2001 Betreuer: Bernard Stauffer Betreuer (extern): George Fankhauser Verantwortlich: Prof. Dr. Albert Kundig¨ Einfuhrung¨ Die Firma acter ag entwickelt eine neuartige Smartcard, die im Gegensatz zur traditionellen Kontaktschnittstelle via Bluetooth drahtlos kommuniziert. Bei dieser neuen Form der Kommunikation ergeben sich v¨ollig neue Proble- me: die Ubertragung¨ ist weder zuverl¨assig noch sicher.
    [Show full text]
  • IRC Channel Data Analysis Using Apache Solr Nikhil Reddy Boreddy Purdue University
    Purdue University Purdue e-Pubs Open Access Theses Theses and Dissertations Spring 2015 IRC channel data analysis using Apache Solr Nikhil Reddy Boreddy Purdue University Follow this and additional works at: https://docs.lib.purdue.edu/open_access_theses Part of the Communication Technology and New Media Commons Recommended Citation Boreddy, Nikhil Reddy, "IRC channel data analysis using Apache Solr" (2015). Open Access Theses. 551. https://docs.lib.purdue.edu/open_access_theses/551 This document has been made available through Purdue e-Pubs, a service of the Purdue University Libraries. Please contact [email protected] for additional information. Graduate School Form 30 Updated 1/15/2015 PURDUE UNIVERSITY GRADUATE SCHOOL Thesis/Dissertation Acceptance This is to certify that the thesis/dissertation prepared By Nikhil Reddy Boreddy Entitled IRC CHANNEL DATA ANALYSIS USING APACHE SOLR For the degree of Master of Science Is approved by the final examining committee: Dr. Marcus Rogers Chair Dr. John Springer Dr. Eric Matson To the best of my knowledge and as understood by the student in the Thesis/Dissertation Agreement, Publication Delay, and Certification Disclaimer (Graduate School Form 32), this thesis/dissertation adheres to the provisions of Purdue University’s “Policy of Integrity in Research” and the use of copyright material. Approved by Major Professor(s): Dr. Marcus Rogers Approved by: Dr. Jeffery L Whitten 3/13/2015 Head of the Departmental Graduate Program Date IRC CHANNEL DATA ANALYSIS USING APACHE SOLR A Thesis Submitted to the Faculty of Purdue University by Nikhil Reddy Boreddy In Partial Fulfillment of the Requirements for the Degree of Master of Science May 2015 Purdue University West Lafayette, Indiana ii To my parents Bhaskar and Fatima: for pushing me to get my Masters before I join the corporate world! To my committee chair Dr.
    [Show full text]
  • An Effective Method for Web Log Preprocessing and Page Access Frequency Using Web Usage Mining
    International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 2 (2018) pp. 1227-1232 © Research India Publications. http://www.ripublication.com An Effective method for Web Log Preprocessing and Page Access Frequency using Web Usage Mining Jayanti Mehra1 Research Scholar, Department of computer Application, Maulana Azad National Institute of Technology (MANIT), Bhopal, Madhya Pradesh, India. Dr. R S Thakur2 Assistant Professor, Department of computer Application, ,Maulana Azad National Institute of Technology (MANIT), Bhopal, Madhya Pradesh, India. Abstract web is huge. A web site is the connection the consumer to company. The companies can revise visitor’s performance World Wide Web is rising rapidly and enormous amount of during web investigation, and discover the patterns. Web information is produced due to user’s communications with mining is generally distinct as find outing and study of helpful web sites. To exploit this information, recognizing usage information commencing the World Wide Web. Web mining pattern of users is very significant. Web Usage Mining is the split into three parts: Web Contents Mining, Web structure application of data mining techniques to find out the useful, mining and Web Usage Mining. Web Contents Mining hidden information about the users and interesting patterns represents the taking out of helpful information and web from data extracted from Web Log files. It supports to know knowledge from web resources or web contents such as text, frequently accessed pages, suppose user navigation, progress image, audio, video, and structured records. Web Usage web site structure etc. In command to relate Web Usage Mining can be as the find and analysis of access patterns of Mining, variety of actions is executed.
    [Show full text]