Search results “Log analysis data mining”
Server Log Analysis with Pandas
Taavi Burns Use iPython, matplotlib, and Pandas to slice, dice, and visualize your application's behaviour through its logs.
Views: 10127 Next Day Video
Mining Your Logs - Gaining Insight Through Visualization
Google Tech Talk (more info below) March 30, 2011 Presented by Raffael Marty. ABSTRACT In this two part presentation we will explore log analysis and log visualization. We will have a look at the history of log analysis; where log analysis stands today, what tools are available to process logs, what is working today, and more importantly, what is not working in log analysis. What will the future bring? Do our current approaches hold up under future requirements? We will discuss a number of issues and will try to figure out how we can address them. By looking at various log analysis challenges, we will explore how visualization can help address a number of them; keeping in mind that log visualization is not just a science, but also an art. We will apply a security lens to look at a number of use-cases in the area of security visualization. From there we will discuss what else is needed in the area of visualization, where the challenges lie, and where we should continue putting our research and development efforts. Speaker Info: Raffael Marty is COO and co-founder of Loggly Inc., a San Francisco based SaaS company, providing a logging as a service platform. Raffy is an expert and author in the areas of data analysis and visualization. His interests span anything related to information security, big data analysis, and information visualization. Previously, he has held various positions in the SIEM and log management space at companies such as Splunk, ArcSight, IBM research, and PriceWaterhouse Coopers. Nowadays, he is frequently consulted as an industry expert in all aspects of log analysis and data visualization. As the co-founder of Loggly, Raffy spends a lot of time re-inventing the logging space and - when not surfing the California waves - he can be found teaching classes and giving lectures at conferences around the world. http://about.me/raffy
Views: 25619 GoogleTechTalks
Log File Frequency Analysis with Python
Information Security professionals often have reason to analyze logs. Whether Red Team or Blue Team, there are countless times that you find yourself using "grep", "tail", "cut", "sort", "uniq", and even "awk"! While these powerful UNIX methods take us far, there is always that time when you want more power! In this webcast, Joff Thyer will discuss using Python regular expressions, and dictionaries to extract useful data for frequency analysis. If you want to learn even more about Python, join Joff for SANS SEC573 - "Automating Information Security with Python" www.sans.org/sec573 Slides available here: https://www.blackhillsinfosec.com/webcast-log-file-frequency-analysis-python/
Spark basic tutorial for log mining
Hello and welcome to this video tutorial on how to use apache spark for log mining. In this demo we are going to see apache spark application for processing large collection of data. This is kind of big data operation, where you have Tera bytes of Data collected over time and want to look for a particular error message. Something similar to what used for Metrix dashboard in normal BI operations. In this demo I’m going to show you how to execute this log mining exercise across spark cluster. It’s a kind of theory of operation.
Views: 2035 Navneet Kumar
Hadoop Tutorial: Analyzing Server Logs
This video explores how to use Hadoop and the Hortonworks Data Platform to analyze server log data and respond quickly to an enterprise security breach. Server logs are computer-generated logs that capture data on the operations of a network. Useful for managing network operations, especially for security and regulatory compliance. Organizations use server logs to answer questions about security and compliance.
Views: 39617 Hortonworks
Web Log Analysis on Spark
This video will familiarize you with the fundamentals of processing unstructured data of log files on the Spark processing platform. Diyotta supports a multitude of data sources such as structured, semi-structured or unstructured. Diyotta has the ability to easily extract, transform and load data into target platforms optimally to gain complete visibility. One of the prevailing industry scenarios in the modern data landscape is that a massive amount of data resides in Data Lakes. Organizations need to perform some data alterations to further take it for analysis and visualizations. In this demonstration, we will review an example of unstructured data of the Server Log file in Hadoop and process it through Spark to produce useful insights and load the data into a Cassandra data object.
Views: 394 Diyotta
Graph Mining for Log Data Presented by David Andrzejewski
This talk discusses a few ways in which machine learning techniques can be combined with human guidance in order to understand what the logs are telling us. Sumo Training: https://www.sumologic.com/learn/training/
Views: 2182 Sumo Logic, Inc.
Views: 384 Hari Sainath
Introduction to Event Log Mining with R
Event logs are everywhere and represent a prime source of Big Data. Event log sources run the gamut from e-commerce web servers to devices participating in globally distributed Internet of Things (IoT) architectures. Even Enterprise Resource Planning (ERP) systems produce event logs! Given the rich and varied data contained in event logs, mining these assets is a critical skill needed by every Data Scientist, Business/Data Analyst, and Program/Product Manager. At this meetup, presenter Dave Langer, will show how easy it is to get started mining your event logs using the OSS tools of R and ProM. Dave will cover the following during the presentation: • The scenarios and benefits of event log mining • The minimum data required for event log mining • Ingesting and analyzing event log data using R • Process Mining with ProM • Event log mining techniques to create features suitable for Machine Learning models • Where you can learn more about this very handy set of tools and techniques *R source code will be made available via GitHub here: https://github.com/EasyD/IntroToEventLogMiningMeetup Find out more about David here: https://www.meetup.com/data-science-dojo/events/235913034/ -- Learn more about Data Science Dojo here: https://hubs.ly/H0hC4gG0 Watch the latest video tutorials here: https://hubs.ly/H0hC5sv0 See what our past attendees are saying here: https://hubs.ly/H0hC4jb0 -- Like Us: https://www.facebook.com/datasciencedojo/ Follow Us: https://twitter.com/DataScienceDojo Connect with Us: https://www.linkedin.com/company/data-science-dojo Also find us on: Google +: https://plus.google.com/+Datasciencedojo Instagram: https://www.instagram.com/data_science_dojo/ Vimeo: https://vimeo.com/datasciencedojo
Views: 7182 Data Science Dojo
The Best Way to Prepare a Dataset Easily
In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating. The challenge for this video is here: https://github.com/llSourcell/prepare_dataset_challenge Carl's winning code: https://github.com/av80r/coaster_racer_coding_challenge Rohan's runner-up code: https://github.com/rhnvrm/universe-coaster-racer-challenge Come join other Wizards in our Slack channel: http://wizards.herokuapp.com/ Dataset sources I talked about: https://github.com/caesar0301/awesome-public-datasets https://www.kaggle.com/datasets http://reddit.com/r/datasets More learning resources: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/ https://www.youtube.com/watch?v=kSslGdST2Ms http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/ http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w Hit the Join button above to sign up to become a member of my channel for access to exclusive content!
Views: 192695 Siraj Raval
Web log mining using Association rule mining
Web log mining using Association rule mining
SmarterStats 6.x Web Log Analytics - The Power of Data Mining
See how data mining your Web logs can assist in truly understanding your traffic and learning visitor behavior, troubleshooting page and server issues, and more. Learn more about SmarterStats 6.x Web Log Analytics at: http://www.smartertools.com/smarterstats
Views: 1499 SmarterTools Inc.
Mining your log files made simple with the ELK Stack
Log files hold a vault of useful information for operations as well as business. Processing your log files could reveal a host of useful information: -Who uses that brand new web solution you have just deployed? -Which country are they in? -What device are they using? -When your host is under load, what else is happening in the environment? -That pesky, intermittent error is back! How do you find it? -Our build server is slow, why? Processing application and server log files used to be such a chore. Getting a unified view of all logs required wizard-like skills! But over the past few years simple and easy solutions have emerged. Best of all, they’re free. That’s right, they’re all open source! Join Chris for a hands-on presentation, where we’ll pull some useful open source Docker containers and set to work making them weave some magic on our log files. Chris covers some Docker Compose basics to get you started with a simple ELK stack (Elastic Search, Log Stash and Kibana), configure some endpoints and setup log shipping (for Linux and Windows) so that we can process log files. He will have you processing logs, and then looking at ways to transform and embellish your log data to add IT operational, as well as business value. Using your transformed data, he also covers visualising the data using Kibana. To close out he will have you considering alternative technologies such as Prometheus and Grafana, and discuss the pros and cons of the different technologies. Mining log files has never been so simple. Join Chris to see how easy it is to get started.
ACM CCS 2017 - DeepLog: Anomaly Detection and Diagnosis from System Logs [...] - Min Du
Presented by Min Du. November 1st, 2017. © 2017 ACM, Inc. All Rights Reserved. www.acm.org
Analyzing Log Data with Spark and Looker
Machines are constantly generating data. Unlocking the value of log files has historically lived in the realm of batch processing. However, emerging technologies have dramatically reduced the latency of event pipelines and have improved interactive analysis and querying. In this webinar, Scott Hoover, Looker Data Scientist, and Daniel Mintz, Chief Data Evangelist at Looker, discuss the specifics of setting up a modern pipeline that collects, processes, and analyzes high-volume, machine-generated data. Topics covered include: - Popular collection mechanisms - Hands-on log-parsing example in Spark - How to utilize Looker to glean insights from event data
Views: 1432 Looker
Usage Log Tracking for Analysis Workspace
Under Admin - Logs - Usage & Access, you can better understand your users' usage of Adobe Analytics. This video focuses specifically on measuring Workspace project usage. You can download the Excel file used in this video at https://adobe.ly/2ygP5ws UPDATE: Since this video was released, we published a guide on how to do robust usage analysis IN Analysis Workspace. Visit adobe.ly/aausage WHAT'S NEXT: The Adobe Analytics product team is working toward building this information more directly into the Analytics UI.
Views: 2036 Adobe Analytics
Analyzing Microsoft IIS Web Logs - Part 1
In this video we discover how to get IIS web logs, how to interpret the content and matching the log entries with Wireshark trace data. The BDS dissector mentioned in the video is available from https://community.tribelab.com/course/view.php?id=32
Views: 2844 TribeLab
Transforming Data - Data Analysis with R
This video is part of an online course, Data Analysis with R. Check out the course here: https://www.udacity.com/course/ud651. This course was designed as part of a program to help you and others become a Data Analyst. You can check out the full details of the program here: https://www.udacity.com/course/nd002.
Views: 58164 Udacity
Implement a data mining approach in order to increase the revenue generation by analysing the users browsing behaviour for the TCP Training Company’s website based on their web log data.
Views: 735 Bajju Sampath
Deciphering Log Files (Using Syslog)
Get my Security+ Cert Guide! https://click.linksynergy.com/link?id=g//2PZbywdw&offerid=163217.2769094&type=2&murl=http%3A%2F%2Fwww.pearsonitcertification.com%2Ftitle%2F9780789758996&u1=secplusCGyoutube Security+ Study Page: https://www.sy0-501.com Demonstrates how to work a common Syslog program and analyze basic log files from a SOHO firewall. - Supports the CompTIA Security+ SY0-401 Cert Guide 3rd Edition, available at Amazon at the following link: http://www.amazon.com/gp/product/0789753332/ref=as_li_tl?ie=UTF8&camp=211189&creative=373489&creativeASIN=0789753332&link_code=as3&tag=davlprostecbl-20&linkId=5A5J7ZXKELKWYM5N - See my main Security+ page for more information: http://www.SY0-401.com
Views: 8193 D Pro Computer
Log Analysis with Elasticsearch, part 1
[Note: for Part 2 click here: https://www.youtube.com/watch?v=lv8gJgPx2cQ] The talk goes through the basics of centralizing logs in Elasticsearch and all the strategies that make it scale with billions of documents in production. For the slides and more content check out: http://blog.sematext.com/2015/10/13/log-analysis-with-elasticsearch/
Views: 7883 Sematext
LISA17 - Fast Log Analysis Made Easy by Automatically Parsing Heterogeneous Logs
Biplob Debnath and Will Dennis, NEC Laboratories America, Inc. @bkdebnath Existing log analysis tools like ELK (Elasticsearch-LogStash-Kibana), VMware LogInsight, Loggly, etc. provide platforms for indexing, monitoring, and visualizing logs. Although these tools allow users to relatively easily perform ad-hoc queries and define rules in order to generate alerts, they do not provide automated log parsing support. In particular, most of these systems use regular expressions (regex) to parse log messages. These tools assume that the administrators know how to work with regex, and make the admins manually parse and define the fields of interest. By definition, these tools support only supervised parsing as human input is essential. However, human involvement is clearly non-scalable for heterogeneous and continuously evolving log message formats in systems such as IoT, and it is humanly impossible to manually review the sheer number of log entries generated in an hour, let alone days and weeks. On top of that, writing regex-based parsing rules is long, frustrating, error-prone, and regex rules may conflict with each other especially for IoT-like systems. In this talk, we describe how we automatically generate regex rules based on the log data, which is described further in our research work, LogMine: Fast Pattern Recognition for Log Analytics, published at the CIKM 2016 conference. We also show a demo to illustrate how to integrate our solution with the popular ELK stack. View the full LISA17 program: https://www.usenix.org/lisa17/program
Views: 968 USENIX
Brilliant & Powerful - All-in-One Log Management and Log Analysis Platform
https://xpolog.com/ - Simplify and empower your log events data mining and analysis with XpoLog technology that automates: *Deployment (3 minutes!) *Troubleshooting & Anomaly Detection *Log analysis - Out of The Box Analytics Apps with Live Reports *Advanced Monitoring and more! Download XpoLog and Deliver Your Best with Ease!
Loglinear Analysis
Views: 3174 Kelsey Hall
Web server log analysis on huge dataset using partitioning approach
For Project Assistance: MBLAZAN R & D Centre Madagadipet, Puducherry - 605 018. Contact: (0)9047970047; (0)9543699666 Email id: [email protected] BlogSpot : http://mblazanrdc.blogspot.in/
Views: 35 Rajakumar R
Startup uses machine learning to identify programming errors with log analytics
Coralogix applies machine learning to the analysis of software logs to find errors quickly. More than 70 percent of resolution time is wasted on discovering errors and bugs in corporate software, while only 30 percent is spent fixing them. The cost of this labor burden is a serious incentive for companies to seek out more advanced solutions. At Coralogix, we recognize that highly paid software engineers shouldn’t have to waste valuable time deciphering boatloads of unstructured string data (the standard format of software logs) when they could focus on creating products instead. They need a tool that will do the slogging for them—and do it right.
Data Mining for Security - Konrad Rieck
Many tasks in computer security revolve around the manual analysis of data, such as the inspection of log files or network traffic. Data mining and machine learning can help to accelerate these tasks and provides versatile tools for detecting and analyzing security data. The sesions deals with the combination of machine learning and computer security. After a short introduction to the basics of machine learning, we present common learning concepts and discuss how they are applied to security problems, such as intrusion detection, malware analysis or vulnerability discovery. I am a Professor of Computer Science at Technische Universität Braunschweig. I am leading the Institute of System Security. Prior to taking this position, I have been working at the University of Göttingen, Technische Universität Berlin and Fraunhofer Institute FIRST. My research interests revolve around computer security and machine learning. This includes the detection of computer attacks, the analysis of malicious software, and the discovery of vulnerabilities. I am also interested in efficient algorithms for analyzing structured data, such as sequences, trees and graphs. My Erdős number is 3 (Müller → Jagota → Erdős). My Bacon number is ∞, though.
Views: 959 secappdev.org
K-Means Clustering Algorithm - Cluster Analysis | Machine Learning Algorithm | Data Science |Edureka
( Data Science Training - https://www.edureka.co/data-science ) This Edureka k-means clustering algorithm tutorial video (Data Science Blog Series: https://goo.gl/6ojfAa) will take you through the machine learning introduction, cluster analysis, types of clustering algorithms, k-means clustering, how it works along with an example/ demo in R. This Data Science with R tutorial video is ideal for beginners to learn how k-means clustering work. You can also read the blog here: https://goo.gl/QM8on4 Subscribe to our channel to get video updates. Hit the subscribe button above. Check our complete Data Science playlist here: https://goo.gl/60NJJS #kmeans #clusteranalysis #clustering #datascience #machinelearning How it Works? 1. There will be 30 hours of instructor-led interactive online classes, 40 hours of assignments and 20 hours of project 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. You will get Lifetime Access to the recordings in the LMS. 4. At the end of the training you will have to complete the project based on which we will provide you a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Data Science course will cover the whole data life cycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modelling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities. - - - - - - - - - - - - - - Why Learn Data Science? Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework. After the completion of the Data Science course, you should be able to: 1. Gain insight into the 'Roles' played by a Data Scientist 2. Analyse Big Data using R, Hadoop and Machine Learning 3. Understand the Data Analysis Life Cycle 4. Work with different data formats like XML, CSV and SAS, SPSS, etc. 5. Learn tools and techniques for data transformation 6. Understand Data Mining techniques and their implementation 7. Analyse data using machine learning algorithms in R 8. Work with Hadoop Mappers and Reducers to analyze data 9. Implement various Machine Learning Algorithms in Apache Mahout 10. Gain insight into data visualization and optimization techniques 11. Explore the parallel processing feature in R - - - - - - - - - - - - - - Who should go for this course? The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics 4. Business Analysts who want to understand Machine Learning (ML) Techniques 5. Information Architects who want to gain expertise in Predictive Analytics 6. 'R' professionals who want to captivate and analyze Big Data 7. Hadoop Professionals who want to learn R and ML techniques 8. Analysts wanting to understand Data Science methodologies For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Customer Reviews: Gnana Sekhar Vangara, Technology Lead at WellsFargo.com, says, "Edureka Data science course provided me a very good mixture of theoretical and practical training. The training course helped me in all areas that I was previously unclear about, especially concepts like Machine learning and Mahout. The training was very informative and practical. LMS pre recorded sessions and assignmemts were very good as there is a lot of information in them that will help me in my job. The trainer was able to explain difficult to understand subjects in simple terms. Edureka is my teaching GURU now...Thanks EDUREKA and all the best. "
Views: 72890 edureka!
Log Analysis - Intern Presentation
Not violating any NDAs, my presentation is uploaded at the request of a few lovely administrators here at Cloudera. In the example log entry to demonstrate the log-cleaning - I replaced the actual IP address with an arbitrary IP Address (1.61803398 is the first 9 digits of the golden ratio). No confidential information is used. The downloads by platform metric is in millions. (i.e. 6 = 6 million downloads)
Views: 2233 Rin Ray
Web server log analysis on huge dataset using partitioning approach
This project is for analyzing webserver log analysis using Apache Hadoop and Hive
Web Mining - Tutorial
Web Mining Web Mining is the use of Data mining techniques to automatically discover and extract information from World Wide Web. There are 3 areas of web Mining Web content Mining. Web usage Mining Web structure Mining. Web content Mining Web content Mining is the process of extracting useful information from content of web document.it may consists of text images,audio,video or structured record such as list & tables. screen scaper,Mozenda,Automation Anywhere,Web content Extractor, Web info extractor are the tools used to extract essential information that one needs. Web Usage Mining Web usage Mining is the process of identifying browsing patterns by analysing the users Navigational behaviour. Techniques for discovery & pattern analysis are two types. They are Pattern Analysis Tool. Pattern Discovery Tool. Data pre processing,Path Analysis,Grouping,filtering,Statistical Analysis, Association Rules,Clustering,Sequential Pattterns,classification are the Analysis done to analyse the patterns. Web structure Mining Web structure Mining is a tool, used to extract patterns from hyperlinks in the web. Web structure Mining is also called link Mining. HITS & PAGE RANK Algorithm are the Popular Web structure Mining Algorithm. By applying Web content mining,web structure Mining & Web usage Mining knowledge is extracted from web data.
Elasticsearch: Analyzing Log Data Using ELK
This video is a sample from Skillsoft's video course catalog. After watching this video, you will be able to demonstrate how to use ELK analysis to monitor and analyze log data. Skillsoft is the global leader in eLearning. We train more professionals than any other company and we are trusted by the world's leading organizations, including 65 percent of the Fortune 500. At Skillsoft, our mission is to build beautiful technology and engaging content. Our 165,000+ courses, videos and books are accessed more than 130 million times every month, in 160 countries and 29 languages. With 100% cloud access, anytime, anywhere.
Views: 2634 Skillsoft YouTube
Log Analysis with Python | An introduction
https://infosecaddicts.com/ In this video we will see how to do arithmetic operations with Python 2, also make a simple print. Python is the next logical step up. If you want to use parsers and scripts for Log Analysis. ---------------------------------------------------------------------------------------- Remember, if you want more information or have questions, suggestions, leave a comment below, or visit our site and social networks. ☑️ InfoSecAddicts Website: https://infosecaddicts.com/ ☑️ 🌐 SOCIAL NETWORKS 🌐 Facebook: https://www.facebook.com/InfoSecAddicts/ 📡 Twitter: https://twitter.com/InfoSecAddicts?s=17 Give us a 👍 🎥 Thanks for watching, and I hope you enjoyed the video. 🎥 🙂
Views: 517 InfoSecAddicts
2014-02-12 CERIAS - Beehive: Large-Scale Log Analysis for Detecting Suspicious Activity in Enterp...
Recorded: 02/12/2014 CERIAS Security Seminar at Purdue University Beehive: Large-Scale Log Analysis for Detecting Suspicious Activity in Enterp... Ting-Fang Yen, RSA As more and more Internet-based attacks arise, organizations are respondingby deploying an assortment of security products that generate situationalintelligence in the form of logs. These logs often contain high volumes ofinteresting and useful information about activities in the network, and areamong the first data sources that information security specialists consultwhen they suspect that an attack has taken place. However, security productsoften come from a patchwork of vendors, and are inconsistently installed andadministered. They generate logs whose formats differ widely and that areoften incomplete, mutually contradictory, and very large in volume. Hence,although this collected information is useful, it is often dirty.We present a novel system, Beehive, that attacks the problem ofautomatically mining and extracting knowledge from the dirty log dataproduced by a wide variety of security products in a large enterprise. Weimprove on signature-based approaches to detecting security incidents andinstead identify suspicious host behaviors that Beehive reports as potentialsecurity incidents. These incidents can then be further analyzed by incidentresponse teams to determine whether a policy violation or attack hasoccurred. We have evaluated Beehive on the log data collected in a largeenterprise, EMC, over a period of two weeks. We compare the incidentsidentified by Beehive against enterprise Security Operations Centerreports, antivirus software alerts, and feedback from enterprise securityspecialists. We show that Beehive is able to identify malicious events andpolicy violations which would otherwise go undetected. Ting-Fang Yen is a research scientist at RSA Laboratories, the security division of EMC. Ting-Fang's research interests include network security and data analysis for security applications. Ting-Fang received a B.S. degree in Computer Science and Information Engineering from National Chiao Tung University, Taiwan, and M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University. (Visit: www.cerias.purdue.edu)
Views: 888 ceriaspurdue
Kirix Strata - Ad Hoc Web Log Analysis
This screencast shows how to extract phpBB forum search terms from an Apache web log. Related blog post here: http://www.kirix.com/blog/2007/08/24/embedded-phpbb-search-terms-within-apache-web-logs/
Views: 1393 kirixcorp
Website / Web Log Analysis - Web Traffic Stats & Page Tracking - Week #26
http://www.26weekplan.com Website / Web Log Analysis - Web Traffic Stats & Page Tracking is Week #26 of the 26-Week Internet Marketing Plan
Views: 1348 David Bain TV
Customer Behaviour Prediction Using Web Usage Mining
Get more details on this system with details at http://nevonprojects.com/customer-behavior-prediction-using-web-usage-mining/ System monitors users web usage data and provides appropriate reporting to admin
Views: 6617 Nevon Projects
System Event Mining: Algorithms and Applications part 1
Authors: Genady Ya. Grabarnik, St. John's University Larisa Shwartz, IBM Thomas J. Watson Research Center Tao Li, Florida International University Abstract: Many systems, from computing systems, physical systems, business systems, to social systems, are only observable indirectly from the events they emit. Events can be defined as real-world occurrences and they typically involve changes of system states. Events are naturally temporal and are often stored as logs, e.g., business transaction logs, stock trading logs, sensor logs, computer system logs, HTTP requests, database queries, network traffic data, etc. These events capture system states and activities over time. For effective system management, a system needs to automatically monitor, characterize, and understand its behavior and dynamics, mine events to uncover useful patterns, and acquire the needed knowledge from historical log/event data. Event mining is a series of techniques for automatically and efficiently extracting valuable knowledge from historical event/log data and plays an important role in system management. The purpose of this tutorial is to present a variety of event mining approaches and applications with a focus on computing system management. It is mainly intended for researchers, practitioners, and graduate students who are interested in learning about the state of the art in event mining. Link to tutorial: https://users.cs.fiu.edu/~taoli/event-mining/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 267 KDD2017 video
How to use Web Log Storming for advanced web analytics
This video shows how to use Web Log Storming log analyzer (http://www.weblogstorming.com/), a software that gives you insights about your website visitors. It quickly goes through basic reports and then presents specific and unique features such are live filtering, drilling down, segmentation, setting goals for conversion calculation, etc.
Views: 1737 Dataland Software
2012-09-06 [2/3] MIA: Monitoring, Instrumentation and (Log) Analysis -- Sean Porter
MIA: Monitoring, Instrumentation and (Log) Analysis -- Sean Porter Monitoring is important, as you can't manage what you haven't measured. There are several ways an application can emit and expose the data that we as developers and operators care about, the most common methods being log streams and instrumentation. Logs can be a treasure trove of information, usually lightly structured, full of metrics, performance and usage indicators. Manual instrumentation is a necessary evil, providing a way to measure what code does when it runs, in production. Knowing application dependencies, understanding their relationships, and monitoring all the way down to the system resources they consume is a MUST. Nothing beats a functional check. There is not one tool to rule them all, think unix toolchain; Logstash, Collectd, Sensu, Graphite, and Gdash. Visibility is important, dashboards are as good as the people watching them. Demo stack included.
Views: 1081 Saem Ghani
Basic Server Log Analysis
Top 5 tips of what to first analyse in your server logs
Views: 382 Coreter Media
Customer Segmentation Using Log Data (Click Stream Analytics) - Happiest Minds
In this video, a case study explained on how Happiest Minds helped an ecommerce company identifying right customer segments using click stream analytics Want to learn more about Click Stream Data Analytics? Watch this video https://www.youtube.com/watch?v=iO85sohKwKg Learn more about Happiest Minds Ecommerce Solutions http://www.happiestminds.com/ecommerce-solutions/ Ecommerce Analytics http://www.happiestminds.com/ecommerce-analytics/ Retail Solutions http://www.happiestminds.com/industries-retail/ Website: http://www.happiestminds.com/ Have a question? Send us on: http://www.happiestminds.com/write%20to%20us Connect with us on: Facebook: https://www.facebook.com/happiestminds Twitter : http://twitter.com/#!/happiestminds LinkedIn : http://www.linkedin.com/company/happiest-minds-technologies Slideshare : http://www.slideshare.net/happiestminds Google + : https://plus.google.com/u/0/+happiestminds/posts
Views: 1380 Happiest Minds