This document discusses personal web usage mining, which involves analyzing individual user's web browsing and navigation data recorded on the client side, rather than server side web logs. It proposes recording both remote activities sent to web servers as well as local on-desktop activities in an activity log. This log, along with cached web pages, would be stored and processed in a data warehouse to facilitate data mining and the development of tools and applications to understand users' interests and enhance their web experience.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
FOCUS K3D is a Coordination Action (CA) of the European Union's 7th Framework Programme which aims at promoting the adoption of best-practices for the use of semantics in 3D content modelling and processing. This slide set gives an overview of the whole project.
You can download these slides at
http://www.focusk3d.eu/downloads
Learning to Classify Users in Online Interaction NetworksSymeon Papadopoulos
Presentation given at ICCSS 2015, Helsinki, Finland. It illustrates an approach for classifying users of OSNs solely based on their interactions with other users.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
Presentation on Web Mining Or Data Mining. Data mining is the process of discovering insightful, interesting, and novel patterns, as well as descriptive, understandable and predictive models from large-scale data. data analysis aims to explore the numeric and categorical attributes of the data individually or jointly to extract key characteristics of the data sample via statistics that give information about the centrality, dispersion, and so on.
FOCUS K3D is a Coordination Action (CA) of the European Union's 7th Framework Programme which aims at promoting the adoption of best-practices for the use of semantics in 3D content modelling and processing. This slide set gives an overview of the whole project.
You can download these slides at
http://www.focusk3d.eu/downloads
Learning to Classify Users in Online Interaction NetworksSymeon Papadopoulos
Presentation given at ICCSS 2015, Helsinki, Finland. It illustrates an approach for classifying users of OSNs solely based on their interactions with other users.
Implementation of Intelligent Web Server Monitoringiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Identifying the Number of Visitors to improve Website Usability from Educatio...Editor IJCATR
Web usage mining deals with understanding the Visitor’s behaviour with a Website. It helps in understanding the concerns
such as present and future probability of every website user, relationship between behaviour and website usability. It has different
branches such as web content mining, web structure and web usage mining. The focus of this paper is on web mining usage patterns of
an educational institution web log data. There are three types of web related log data namely web access log, error log and proxy log
data. In this paper web access log data has been used as dataset because the web access log data is the typical source of navigational
behaviour of the website visitor. The study of web server log analysis is helpful in applying the web mining techniques.
Automatic recommendation for online users using web usage miningIJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Automatic Recommendation for Online Users Using Web Usage Mining IJMIT JOURNAL
A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user’s navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user’s having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
A 101 level introduction to Web Analytics, and the concepts that underlie the technical aspects of digital measurement. Aimed at professionals new to the space, or business people looking to understand Web Analytics at a more detailed level.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
2. Web Usage Mining
The discovery of patterns in the browsing and navigation
data of Web users.
Web usage mining has been an important technology for
understanding user’s behaviors on the Web.
Currently, most Web usage mining research has been
focusing on the Web server side.
The main purpose of research is to improve a Web site’s
service and the server’s performance.
Data sources for Web usage mining are primarily Web
server logs. Although it is very important and interesting
to investigate server side issues, we argue that an
equally important and potentially fruitful aspect of Web
usage mining is the mining of client side usage data.
3. Web Usage Mining on Server Side
Currently, Web usage mining finds patterns in Web server
logs. The logs are preprocessed to group requests from
the same user into sessions.
A session contains the requests from a single visit of a
user to the Web site. During the preprocessing, irrelevant
information for Web usage mining such as background
images and unsuccessful requests is ignored. The users
are identified by the IP addresses in the log and all
requests from the same IP address within a certain time-
window are put into a session.
Different heuristics have been developed to deal with the
inaccuracy due to caching, IP sharing or blocking, and
network congestion.
4. Web Usage Mining on Server Side
Some common characteristics
• Their goal is to improve Web services and performance
Through the improvement of Web sites, including their
contents, structure, presentation, and delivery.
• They focus on the mining of server side data. Their data
sources are almost exclusively server logs, sometimes with
site structure and/or page contents.
• They target groups of users instead of individual users. It is
overwhelming for a Web site to deal with users on an
individual basis.
5. Personal Web Usage Mining
Individual’s Web usage, rather than group behaviors.
By looking into a user’s Web usage data, we hope to
understand the user’s interests, behaviors, and
preferences.
In other words, we are building the user’s Web profile.
We call this personal Web usage mining since it focuses
on personal Web usage.
6. Personal Web Usage Mining
Some of the reasons we advocate personal Web usage
mining are as follows.
• The goal of personal Web usage mining is to help and
enhance individual users Web use. It intends to make the
Web easier to use from a single user’s point of view.
• Client side data provide a more accurate and complete
picture of a user’s Web activities.
• We can achieve true individualism and personalization.
● Users have full control of what, when, and how their
data can be used for mining.
● Personal Web usage has increased significantly
recently,
7. Personal Web Usage Mining
Some researchers are building intelligent agents or
Internet agents that will help individuals use the Web. For
example, many agents were built for information filtering
and gathering on the Web.
WARREN is a multi-agent system for compiling financial
information.
WEBMATE edits a personal newpaper.
WebSifter is a meta-search agent which uses taxonomy
to improve search on the Web.
Other examples include home page finder , user
interface learning agent, and Web browsing assistant.
Although some aspects and pieces of personal Web
usage mining may be around in various areas such as
intelligent agent, Web warehousing, and Web usage
mining,
8. Personal Web Usage Mining
Two kinds of user Web activities are recorded for
analysis:
● The remote activities
include requests sent by a user to a Web server. Such
kind of click stream data includes the URLs of pages as
well as any keywords, queries, forms, and cookies sent
with the URL.
The remote activities can be captured by almost all Web
browsers. Besides, the browsers also cache the Web pages
in most cases.
9. Personal Web Usage Mining
Two kinds of user Web activities are recorded for
analysis:
● The local activities
include actions the user can take at his or her desktop
without the knowledge of Web servers. They include,
but are not limited to, the following.
Save a page, Print a page, Click Back on browser, Click Forward on
browser, Click Reload on browser, Click Stop on browser, Email a
link/page, Add a bookmark, Minimize/maximize/close window, Change
visual settings such as font size.
The local activities can be recorded by an activity recorder, which is a
client side program running on top of the browser.
10. Personal Web Usage Mining
These two kinds of activities are put together into an
activity log. Each entry in the activity log will contain a
timestamp and an activity. Some will contain extra
information such as URL, cache address,keyword,
cookie, email address, and font size.
The schema of the log looks like this:
(timestamp, activity, [URL], [cache address],[keyword],
[cookie], [email address], [other optional fields])
11. Personal Web Usage Mining
There are four major modules in the framework:
● Logging
● Data Warehousing
● Data mining
● Tool/Application.
12. Personal Web Usage Mining
In the logging module, user Web activities are stored
into the activity, as well as the cached pages.
In the data warehousing module, the logs and cached
pages are cleansed, extracted, transformed, aggregated,
and stored in a data warehouse. The data warehouse will
facilitate search, query, and OLAP operations, in the
mean time providing data sources for mining.
In the data mining module, various data mining
algorithms are applied to the data in the data warehouse,
whose findings will be used by the tools and applications
in the tool/application module.