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.
COMPARISON ANALYSIS OF WEB USAGE MINING USING PATTERN RECOGNITION TECHNIQUESIJDKP
This document summarizes a research paper that analyzes web usage mining using pattern recognition techniques. It discusses how web logs from the NASA website were preprocessed and then analyzed using a web log exploration tool. Key patterns discovered include determining most visitors were from the US/Canada based on IP addresses, most common file type accessed was images (especially GIF files), and GIF files were most popular on Thursdays at noon. The paper concludes the techniques help understand user behavior and improve website performance and organization strategies.
Web Data mining-A Research area in Web usage miningIOSR Journals
This document provides a summary and analysis of web usage mining systems and technologies. It begins with an introduction to web mining and discusses the three main categories: web content mining, web structure mining, and web usage mining. The majority of the document then focuses on web usage mining, covering the concepts, typical data sources, log formats, preprocessing approaches including data cleaning, user/session identification and path completion, knowledge discovery methods, and pattern analysis. It also provides details on an online web personalization system called SUGGEST that utilizes these techniques to provide personalized recommendations to users.
The document describes a proposed algorithm called Visitors' Online Behavior (VOB) for tracing visitors' online behaviors to effectively mine web usage data. The VOB algorithm identifies user behavior, creates user and page clusters, and determines the most and least popular web pages. It discusses how web usage mining analyzes user behavior logs to discover patterns. Preprocessing techniques like data cleaning, user/session identification, and path completion are applied to web server logs to maximize accurate pattern mining. Existing algorithms are described that apply preprocessing concepts to calculate unique user counts, minimize log file sizes, and identify user sessions.
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.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
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.
The PagePrompter system uses data mining techniques to create an intelligent agent that provides recommendations to users navigating a website. It has three main modules: 1) The usage mining module analyzes web logs to find frequent patterns and association rules using Apriori and clusters pages using leader clustering and C4.5. 2) The recommendation module provides suggestions to users based on their actions and the database. 3) The adaptive pages module generates customized pages and interfaces with the database. PagePrompter aims to help users efficiently find information on a site by learning from usage data and behavior.
This document proposes a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model uses a multi-layered network architecture with backpropagation learning to analyze web log data. Data preprocessing steps like cleaning, user identification, and transaction identification are applied to prepare the enterprise proxy log data for analysis. The proposed framework aims to discover useful patterns from web log data through a combination of K-means clustering and a feedforward neural network.
COMPARISON ANALYSIS OF WEB USAGE MINING USING PATTERN RECOGNITION TECHNIQUESIJDKP
This document summarizes a research paper that analyzes web usage mining using pattern recognition techniques. It discusses how web logs from the NASA website were preprocessed and then analyzed using a web log exploration tool. Key patterns discovered include determining most visitors were from the US/Canada based on IP addresses, most common file type accessed was images (especially GIF files), and GIF files were most popular on Thursdays at noon. The paper concludes the techniques help understand user behavior and improve website performance and organization strategies.
Web Data mining-A Research area in Web usage miningIOSR Journals
This document provides a summary and analysis of web usage mining systems and technologies. It begins with an introduction to web mining and discusses the three main categories: web content mining, web structure mining, and web usage mining. The majority of the document then focuses on web usage mining, covering the concepts, typical data sources, log formats, preprocessing approaches including data cleaning, user/session identification and path completion, knowledge discovery methods, and pattern analysis. It also provides details on an online web personalization system called SUGGEST that utilizes these techniques to provide personalized recommendations to users.
The document describes a proposed algorithm called Visitors' Online Behavior (VOB) for tracing visitors' online behaviors to effectively mine web usage data. The VOB algorithm identifies user behavior, creates user and page clusters, and determines the most and least popular web pages. It discusses how web usage mining analyzes user behavior logs to discover patterns. Preprocessing techniques like data cleaning, user/session identification, and path completion are applied to web server logs to maximize accurate pattern mining. Existing algorithms are described that apply preprocessing concepts to calculate unique user counts, minimize log file sizes, and identify user sessions.
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.
A detail survey of page re ranking various web features and techniquesijctet
This document discusses techniques for page re-ranking on websites based on user behavior analysis. It describes how web usage mining involves analyzing web server logs to extract patterns in user behavior. Common techniques discussed for page re-ranking include Markov models, data mining approaches like clustering and association rule mining, and analyzing linked web page structures. The goal is to better understand user interests and predict future page access to improve information retrieval and optimize website design.
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.
The PagePrompter system uses data mining techniques to create an intelligent agent that provides recommendations to users navigating a website. It has three main modules: 1) The usage mining module analyzes web logs to find frequent patterns and association rules using Apriori and clusters pages using leader clustering and C4.5. 2) The recommendation module provides suggestions to users based on their actions and the database. 3) The adaptive pages module generates customized pages and interfaces with the database. PagePrompter aims to help users efficiently find information on a site by learning from usage data and behavior.
This document proposes a new technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The proposed model uses a multi-layered network architecture with backpropagation learning to analyze web log data. Data preprocessing steps like cleaning, user identification, and transaction identification are applied to prepare the enterprise proxy log data for analysis. The proposed framework aims to discover useful patterns from web log data through a combination of K-means clustering and a feedforward neural network.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
This document discusses improving web performance through prefetching frequently accessed pages. It begins by introducing the concept of prefetching web pages to reduce latency. Next, it reviews related work on predictive prefetching using techniques like Markov models and association rules to predict future page access. Finally, it proposes an approach to increase web performance by analyzing user access logs and website structure to predict pages for prefetching. The goal is to reduce latency and improve user experience by prefetching relevant pages in the background.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effectiv
IRJET- Enhancing Prediction of User Behavior on the Basic of Web LogsIRJET Journal
The document discusses predicting user behavior based on web logs. It proposes using several algorithms to analyze web log data, including Apriori, KNN, FP-Growth, and an Improved Parallel FP-Growth algorithm. The algorithms are applied to preprocessed web log data to identify frequent patterns and items that provide insights into user behavior. Experimental results show the Improved Parallel FP-Growth algorithm provides higher mining efficiency and can handle large, growing datasets.
a novel technique to pre-process web log data using sql server management studioINFOGAIN PUBLICATION
This document summarizes a research paper that proposes a novel technique for pre-processing web log data using SQL Server Management Studio. The paper first discusses how web log data contains irrelevant information that needs to be cleaned through pre-processing before analysis. It then describes the contents of a typical web log file and provides a sample of raw web log data. The paper presents an algorithm for data cleaning and implements it using SQL queries to clean the web log data by removing records with certain file extensions and incomplete URLs. It shows that the data was reduced from over 200,000 records to around 25,000 after pre-processing. The paper concludes that pre-processing is an important step for filtering and organizing data before applying data mining techniques.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
Data mining in web search engine optimizationBookStoreLib
This document presents a proposed approach for optimizing web search by incorporating user feedback to improve result rankings. The approach uses keyword analysis on the user query to initially retrieve and rank relevant web pages. It then analyzes user responses like likes/dislikes and visit counts to update the page rankings. Experimental results on sample education queries show how page rankings change as user responses increase likes for certain pages. The approach aims to provide more useful search results by better reflecting individual user preferences.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
AN INTELLIGENT OPTIMAL GENETIC MODEL TO INVESTIGATE THE USER USAGE BEHAVIOUR ...ijdkp
The unexpected wide spread use of WWW and dynamically increasing nature of the web creates new
challenges in the web mining since the data in the web inherently unlabelled, incomplete, non linear, and
heterogeneous. The investigation of user usage behaviour on WWW is real time problem which involves
multiple conflicting measures of performance. These measures make not only computational intensive but
also needs to the possibility of be unable to find the exact solution. Unfortunately, the conventional methods
are limited to optimization problems due to the absence of semantic certainty and presence of human
intervention. In handling such data and overcome the limitations of conventional methodologies it is
necessary to use a soft computing model that can work intelligently to attain optimal solution.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This paper focuses on various ways of monitoring and tracking of users while surfing the web as well as current methods used by websites to track users. This paper further went on to enumerate how users can protect themselves from being tracked as well as highlight the importance of privacy.
A Comparative Study of Recommendation System Using Web Usage Mining Editor IJMTER
Web Mining is one of the Developing field in research. Exact custom of the Web is to get the
beneficial material in the sites. To reduce the work time of user the Web Usage Mining (WUM) technique
is introduced. In this Technique use Web Page recommendation for the Web request from the user. For
the recommendation system in Web Usage Mining (WUM) variousauthor has introduce different
Algorithm and technique to improve the user interest in surfing the Web. Web log files are used todefine
the user interest and there next recommend page to view.The data stored in the web log file consist of
large amount oferoded, incomplete, and unnecessary information. So, the Web log files have to preprocess, customize, and to clean the data. In this paper we will survey different recommendation technique
to identify the issues in web surfing and to improve web usagemining (WUM) pre-processing for pattern
mining and analysis.
This document summarizes research on improving web performance through integrated web prefetching and caching. It discusses how web prefetching can reduce latency by predicting and fetching web pages before they are requested. An integrated architecture reserves cache space for prefetched pages. By analyzing web logs to build prediction models of frequent paths, it aims to improve performance over caching alone. The tradeoff between reduced latency and potential increased network load is analyzed. Several previous works studying prefetching and caching algorithms individually are reviewed. The goal is a seamless prefetching system that works with existing caching systems.
Implementation of Intelligent Web Server Monitoringiosrjce
This document discusses the implementation of intelligent web server monitoring. It begins by introducing how web usage mining can be used to analyze user access patterns on a website to improve website design. It then discusses how client-side data collection using AJAX can more precisely calculate page browsing times compared to only using server log files. The document provides details on data sources for web usage mining, common log file formats, and examples of web usage mining techniques like statistical analysis, graph mining, and sequential pattern mining. It also reviews related literature on web usage mining and discusses how AJAX works to facilitate client-side data collection.
This document summarizes techniques for monitoring web server usage through web usage mining and statistical analysis. It discusses collecting data from server logs, client-side through scripts and modified browsers, and proxies. Server logs only provide partial information and client-side collection requires user cooperation. The combination of statistical analysis and web usage mining using both server and client-side data can provide powerful insights into how users browse websites to help evaluate websites and improve their design.
Performance of Real Time Web Traffic Analysis Using Feed Forward Neural Netw...IOSR Journals
This document discusses using feed forward neural networks and K-means clustering to analyze real-time web traffic. It proposes a technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The model uses a multi-layered network architecture with backpropagation learning to discover and analyze knowledge from web log data. It also discusses preprocessing the web log data through cleaning, user identification, filtering, session identification and transaction identification before applying the neural network and K-means algorithms.
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
The PagePrompter system uses data mining techniques to create an intelligent agent that provides recommendations to users navigating a website. It has three main modules: 1) The usage mining module analyzes web logs to find patterns like association rules and page clusters using algorithms like Apriori and leader clustering. 2) The recommendation module provides suggestions to users based on their actions and patterns learned from the logs. 3) The adaptive pages module generates customized pages for users based on their profiles and behavior. The system aims to help users efficiently find information on a site by learning from log data and user interactions.
applyingwebminingapplicationforuserbehaviorunderstanding-131215105223-phpapp0...Zakaria Zubi
This document discusses applying web mining techniques to understand user behavior by analyzing web server log files. It describes the phases of web usage mining as including data preprocessing, pattern discovery, and pattern analysis. Data preprocessing involves cleaning the log files, identifying page views, users, and sessions. Pattern discovery applies techniques like association rule mining and classification to find patterns in user behavior. The results section shows applying association rule mining to a transactional database of user sessions to find rules of user behavior. The conclusion emphasizes that web logs contain valuable information about user behavior and different data mining methods can be used to analyze the data.
In this world of information technology, everyone has the tendency to do business electronically. Today
lot of businesses are happening on World Wide Web (WWW), it is very important for the website owner to
provide a better platform to attract more customers for their site. Providing information in a better way is
the solution to bring more customers or users. Customer is the end-user, who accessing the information
in a way it yields some credit to the web site owners. In this paper we define web mining and present a
method to utilize web mining in a better way to know the users and website behaviour which in turn
enhance the web site information to attract more users. This paper also presents an overview of the
various researches done on pattern extraction, web content mining and how it can be taken as a catalyst
for E-business.
3 iaetsd semantic web page recommender systemIaetsd Iaetsd
This document discusses a semantic web-page recommender system that aims to improve upon traditional recommender systems. It proposes two novel knowledge representation models: 1) a semantic network that represents domain knowledge through terms, webpages, and relationships automatically constructed from website data, and 2) a conceptual prediction model that integrates semantic knowledge with web usage data to create a weighted semantic network for making recommendations. The system seeks to overcome limitations of prior systems by automating knowledge base construction and addressing the "new-item problem" through incorporation of semantic information. Evaluation shows the proposed approach yields better performance than existing web usage-based recommender systems.
This document discusses improving web performance through prefetching frequently accessed pages. It begins by introducing the concept of prefetching web pages to reduce latency. Next, it reviews related work on predictive prefetching using techniques like Markov models and association rules to predict future page access. Finally, it proposes an approach to increase web performance by analyzing user access logs and website structure to predict pages for prefetching. The goal is to reduce latency and improve user experience by prefetching relevant pages in the background.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effectiv
IRJET- Enhancing Prediction of User Behavior on the Basic of Web LogsIRJET Journal
The document discusses predicting user behavior based on web logs. It proposes using several algorithms to analyze web log data, including Apriori, KNN, FP-Growth, and an Improved Parallel FP-Growth algorithm. The algorithms are applied to preprocessed web log data to identify frequent patterns and items that provide insights into user behavior. Experimental results show the Improved Parallel FP-Growth algorithm provides higher mining efficiency and can handle large, growing datasets.
a novel technique to pre-process web log data using sql server management studioINFOGAIN PUBLICATION
This document summarizes a research paper that proposes a novel technique for pre-processing web log data using SQL Server Management Studio. The paper first discusses how web log data contains irrelevant information that needs to be cleaned through pre-processing before analysis. It then describes the contents of a typical web log file and provides a sample of raw web log data. The paper presents an algorithm for data cleaning and implements it using SQL queries to clean the web log data by removing records with certain file extensions and incomplete URLs. It shows that the data was reduced from over 200,000 records to around 25,000 after pre-processing. The paper concludes that pre-processing is an important step for filtering and organizing data before applying data mining techniques.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
This document discusses an enhanced approach for detecting user behavior through country-wise local search. It begins with an abstract describing the development of the web and challenges in the field. It then discusses various techniques for web mining including web usage mining, web content mining, and web structure mining. It also discusses sequential pattern mining algorithms and procedures for recommendation systems. The key contribution is proposing a new local search algorithm for country-wise search to make searching more efficient based on local results.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
This document discusses using data mining and k-means cluster analysis to classify search engine optimization (SEO) techniques. It begins with an introduction to SEO and data mining. The paper aims to analyze various SEO techniques used by webmasters and classify them using a data mining approach. Specifically, it uses k-means cluster analysis on SEO techniques to group similar techniques together and identify those with the biggest impact on webpage ranking. The literature review covers past work analyzing SEO techniques and using data mining methods like clustering for search engine optimization.
Data mining in web search engine optimizationBookStoreLib
This document presents a proposed approach for optimizing web search by incorporating user feedback to improve result rankings. The approach uses keyword analysis on the user query to initially retrieve and rank relevant web pages. It then analyzes user responses like likes/dislikes and visit counts to update the page rankings. Experimental results on sample education queries show how page rankings change as user responses increase likes for certain pages. The approach aims to provide more useful search results by better reflecting individual user preferences.
IRJET-A Survey on Web Personalization of Web Usage MiningIRJET Journal
S.Jagan, Dr.S.P.Rajagopalan "A Survey on Web Personalization of Web Usage Mining", International Research Journal of Engineering and Technology (IRJET),Volume 2,issue-01 Mar-2015. e-ISSN:2395-0056, p-ISSN:2395-0072. www.irjet.net , published by Fast Track Publications
Abstract
Now a day, World Wide Web (www) is a rich and most powerful source of information. Day by day it is becoming more complex and expanding in size to get maximum information details online. However, it is becoming more complex and critical task to retrieve exact information expected by its users. To deal with this problem one more powerful concept is personalization which is becoming more powerful now days. Personalization is a subclass of information filtering system that seek to predict the 'ratings' or 'preferences' that a user would give to an items, they had not yet considered, using a model built from the characteristics of an item (content-based approaches or collaborative filtering approaches). Web mining is an emerging field of data mining used to provide personalization on the web. It consist three major categories i.e. Web Content Mining, Web Usage Mining, and Web Structure Mining. This paper focuses on web usage mining and algorithms used for providing personalization on the web.
AN INTELLIGENT OPTIMAL GENETIC MODEL TO INVESTIGATE THE USER USAGE BEHAVIOUR ...ijdkp
The unexpected wide spread use of WWW and dynamically increasing nature of the web creates new
challenges in the web mining since the data in the web inherently unlabelled, incomplete, non linear, and
heterogeneous. The investigation of user usage behaviour on WWW is real time problem which involves
multiple conflicting measures of performance. These measures make not only computational intensive but
also needs to the possibility of be unable to find the exact solution. Unfortunately, the conventional methods
are limited to optimization problems due to the absence of semantic certainty and presence of human
intervention. In handling such data and overcome the limitations of conventional methodologies it is
necessary to use a soft computing model that can work intelligently to attain optimal solution.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
This paper focuses on various ways of monitoring and tracking of users while surfing the web as well as current methods used by websites to track users. This paper further went on to enumerate how users can protect themselves from being tracked as well as highlight the importance of privacy.
A Comparative Study of Recommendation System Using Web Usage Mining Editor IJMTER
Web Mining is one of the Developing field in research. Exact custom of the Web is to get the
beneficial material in the sites. To reduce the work time of user the Web Usage Mining (WUM) technique
is introduced. In this Technique use Web Page recommendation for the Web request from the user. For
the recommendation system in Web Usage Mining (WUM) variousauthor has introduce different
Algorithm and technique to improve the user interest in surfing the Web. Web log files are used todefine
the user interest and there next recommend page to view.The data stored in the web log file consist of
large amount oferoded, incomplete, and unnecessary information. So, the Web log files have to preprocess, customize, and to clean the data. In this paper we will survey different recommendation technique
to identify the issues in web surfing and to improve web usagemining (WUM) pre-processing for pattern
mining and analysis.
This document summarizes research on improving web performance through integrated web prefetching and caching. It discusses how web prefetching can reduce latency by predicting and fetching web pages before they are requested. An integrated architecture reserves cache space for prefetched pages. By analyzing web logs to build prediction models of frequent paths, it aims to improve performance over caching alone. The tradeoff between reduced latency and potential increased network load is analyzed. Several previous works studying prefetching and caching algorithms individually are reviewed. The goal is a seamless prefetching system that works with existing caching systems.
Implementation of Intelligent Web Server Monitoringiosrjce
This document discusses the implementation of intelligent web server monitoring. It begins by introducing how web usage mining can be used to analyze user access patterns on a website to improve website design. It then discusses how client-side data collection using AJAX can more precisely calculate page browsing times compared to only using server log files. The document provides details on data sources for web usage mining, common log file formats, and examples of web usage mining techniques like statistical analysis, graph mining, and sequential pattern mining. It also reviews related literature on web usage mining and discusses how AJAX works to facilitate client-side data collection.
This document summarizes techniques for monitoring web server usage through web usage mining and statistical analysis. It discusses collecting data from server logs, client-side through scripts and modified browsers, and proxies. Server logs only provide partial information and client-side collection requires user cooperation. The combination of statistical analysis and web usage mining using both server and client-side data can provide powerful insights into how users browse websites to help evaluate websites and improve their design.
Performance of Real Time Web Traffic Analysis Using Feed Forward Neural Netw...IOSR Journals
This document discusses using feed forward neural networks and K-means clustering to analyze real-time web traffic. It proposes a technique to enhance the learning capabilities and reduce the computation intensity of a competitive learning multi-layered neural network using the K-means clustering algorithm. The model uses a multi-layered network architecture with backpropagation learning to discover and analyze knowledge from web log data. It also discusses preprocessing the web log data through cleaning, user identification, filtering, session identification and transaction identification before applying the neural network and K-means algorithms.
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
The PagePrompter system uses data mining techniques to create an intelligent agent that provides recommendations to users navigating a website. It has three main modules: 1) The usage mining module analyzes web logs to find patterns like association rules and page clusters using algorithms like Apriori and leader clustering. 2) The recommendation module provides suggestions to users based on their actions and patterns learned from the logs. 3) The adaptive pages module generates customized pages for users based on their profiles and behavior. The system aims to help users efficiently find information on a site by learning from log data and user interactions.
applyingwebminingapplicationforuserbehaviorunderstanding-131215105223-phpapp0...Zakaria Zubi
This document discusses applying web mining techniques to understand user behavior by analyzing web server log files. It describes the phases of web usage mining as including data preprocessing, pattern discovery, and pattern analysis. Data preprocessing involves cleaning the log files, identifying page views, users, and sessions. Pattern discovery applies techniques like association rule mining and classification to find patterns in user behavior. The results section shows applying association rule mining to a transactional database of user sessions to find rules of user behavior. The conclusion emphasizes that web logs contain valuable information about user behavior and different data mining methods can be used to analyze the data.
Applying web mining application for user behavior understandingZakaria Zubi
This document discusses applying web mining techniques to understand user behavior by analyzing server log files. It describes how web usage mining involves three phases: data preprocessing, pattern discovery, and pattern analysis. In data preprocessing, log files are cleaned and parsed to identify users, sessions, and page views. Pattern discovery applies techniques like association rule mining and classification to find relationships between frequently accessed page types and predict future page views. Pattern analysis validates and interprets the discovered patterns to model user behavior and create visualizations. The document provides an example of using association rule mining on a transactional database of user sessions to find patterns in user behavior.
International conference On Computer Science And technologyanchalsinghdm
ICGCET 2019 | 5th International Conference on Green Computing and Engineering Technologies. The conference will be held on 7th September - 9th September 2019 in Morocco. International Conference On Engineering Technology
The conference aims to promote the work of researchers, scientists, engineers and students from across the world on advancement in electronic and computer systems.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
Farthest first clustering in links reorganizationIJwest
Website can be easily design but to efficient user navigation is not a easy task since user behavior is keep changing and developer view is quite different from what user wants, so to improve navigation one way is reorganization of website structure. For reorganization here proposed strategy is farthest first traversal clustering algorithm perform clustering on two numeric parameters and for finding frequent traversal path of user Apriori algorithm is used. Our aim is to perform reorganization with fewer changes in website structure.
MULTIFACTOR NAÏVE BAYES CLASSIFICATION FOR THE SLOW LEARNER PREDICTION OVER M...ijcsa
The high school students must be observed for their slow learning or quick learning abilities to provide
them with the best education practices. Such analysis can be perfectly performed over the student
performance data. The high school student data has been obtained from the schools from the various
regions in Punjab, a pivotal state of India. The complete student data and the selective data of almost 1300
students obtained from one school in the regions has been undergone the test using the proposed model in
this paper. The proposed model is based upon the naïve bayes classification model for the data
classification using the multi-factor features obtained from the input dataset. The subject groups have been
divided into the two primary groups: difficult and normal. The classification algorithm has been applied
individually over data grouped in the various subject groups. Both of the early stage classification events
have produced the almost similar results, whereas the results obtained from the classification events over
the averaging factors and the floating factors told the different story than the early stage classification. The
proposed model results have shown that the deep analysis of the data tells the in-depth facts from the input
data. The proposed model can be considered as the effective classification model when evaluated from the
results described in the earlier sections.
Certain Issues in Web Page Prediction, Classification and Clustering in Data ...IJAEMSJORNAL
Nowadays, data mining which is a part of web mining plays a vital role in various applications such as search engines, health care centers for extracting the individual patient details among huge database, analyzing disease based on basic criteria, education system for analyzing their performance level with other system, social networking, E-Commerce and knowledge management etc., which extract the information based on the user query. The issues are time taken to mine the target content or webpage from the search engines, space complexity and predicting the frequent webpage for the next user based on users’ behaviour.
Enactment of Firefly Algorithm and Fuzzy C-Means Clustering For Consumer Requ...IRJET Journal
The document proposes a novel methodology for predicting consumer demand and future requests on web pages using a hybrid approach. It first classifies consumers as potential or non-potential using a firefly-based neural network with Levenberg-Marquardt algorithm. Potential consumer data is then clustered using an improved fuzzy C-means clustering algorithm. Finally, upcoming consumer demand is predicted by analyzing patterns and recommending web pages with higher weights. The proposed approach is implemented in Java and CloudSim and aims to overcome limitations of existing recommendation systems by providing more accurate and efficient predictions in shorter time.
A Survey on: Utilizing of Different Features in Web Behavior PredictionEditor IJMTER
As the web user increases day by day, there are many websites which have a large
number of visitors at the same instant. So handing of these user required different technique. Out of
these requirements one emerging field is next page prediction, where as per the user navigation
pattern different features has been studied and predict the next page for the user. By this overall web
server response time is reduce. In this paper a detailed study of the different researcher paper has
shown, there techniques outcomes and list of features utilization such as web structure, web log, web
content.
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.
A Novel Method for Data Cleaning and User- Session Identification for Web MiningIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
An effective search on web log from most popular downloaded contentijdpsjournal
A Web page recommender system effectively predicts the best related web page to search. While search
ing
a word from search engine it may display some unnecessary links and unrelated data’s to user so to a
void
this problem, the con
ceptual prediction model combines both the web usage and domain knowledge. The
proposed conceptual prediction model automatically generates a semantic network of the semantic Web
usage knowledge, which is the integration of domain knowledge and web usage i
nformation. Web usage
mining aims to discover interesting and frequent user access patterns from web browsing data. The
discovered knowledge can then be used for many practical web applications such as web
recommendations, adaptive web sites, and personali
zed web search and surfing
Ähnlich wie Automatic recommendation for online users using web usage mining (18)
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
Traditionally, teaching has been centered around classroom delivery. However, the onslaught of the
COVID-19 pandemic has cultivated usage of technology, teaching, and learning methodologies for course
delivery. We investigate and describe different modes of course delivery that maintain the integrity of
teaching and learning. This paper answers to the research questions: 1) What course delivery method our
academic institutions use and why? 2) How can instructors validate the guidelines of the institutions? 3)
How courses should be taught to provide student learning outcomes? Using the Learning Environment
Modeling Language (LEML), we investigate the design and implementation of courses for delivery in the
following environments: face-to-face, online synchronous, asynchronous, hybrid, and hyflex. A good
course design and implementation are key components of instructional alignment. Furthermore, we
demonstrate how to design, implement, and deliver courses in synchronous, asynchronous, and hybrid
modes and describe our proposed enhancements to LEML.
Novel R&D Capabilities as a Response to ESG Risks-Lessons From Amazon’s Fusio...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly open access peer-reviewed journal that publishes articles that contribute new results in all areas of the strategic application of information technology (IT) in organizations. The journal focuses on innovative ideas and best practices in using IT to advance organizations – for-profit, non-profit, and governmental. The goal of this journal is to bring together researchers and practitioners from academia, government, and industry to focus on understanding both how to use IT to support the strategy and goals of the organization and to employ IT in new ways to foster greater collaboration, communication, and information sharing both within the organization and with its stakeholders. The International Journal of Managing Information Technology seeks to establish new collaborations, new best practices, and new theories in these areas.
NOVEL R & D CAPABILITIES AS A RESPONSE TO ESG RISKS- LESSONS FROM AMAZON’S FU...IJMIT JOURNAL
Environmental, Social, and Governance (ESG) management is essential for transforming corporate
financial performance-oriented business strategies into Finance (F) + ESG optimization strategies to
achieve the Sustainable Development Goals (SDGs).
In this trend, the rise of ESG risks has divided firms into two categories. Former incorporates a growthmindset that creates a passion for learning, and urges it to improve itself by endeavoring Research and
development (R&D) -driven challenges, while the other category, characterized by risk aversion, avoids
challenging highly uncertain R&D activities and seeks more manageable endeavors.
This duality underscores the complexity of corporate R&D strategies in addressing ESG risks and
necessitates the development of novel R&D capabilities for corporate R&D transformation strategies
towards F + ESG optimization.
Building on this premise, this paper conducts an empirical analysis, utilizing reliable firms data on ESG
risk and brand value, with a focus on 100 global R&D leader firms. It analyzes R&D and actions for ESG
risk mitigation, and assesses the development of new functions that fulfill F + ESG optimization through
R&D. The analysis also highlights the significance of network externality effects, with a specific focus on
Amazon, a leading R&D company, providing insights into the direction for transforming R&D strategies
towards F + ESG optimization.
The dynamics of stakeholder engagement in F + ESG optimization are indicated with the example of
amazon's activities. Through the analysis, it became evident that Amazon's capacity encompassing growth
and scalability, specifically its ability to grow and expand, is accelerating high-level research and
development by gaining the trust of stakeholders in the "synergy through R&D-driven ESG risk
mitigation."
Finally, as examples of these initiatives, the paper discussed the Climate Pledge led by Amazon and the
transformation of Japan's management system.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
INTRUSION DETECTION SYSTEM USING CUSTOMIZED RULES FOR SNORTIJMIT JOURNAL
This document proposes an intrusion detection system using customized rules for the Snort tool to improve security. The system uses Wireshark to scan network traffic for anomalies, Snort to detect attacks using customized rulesets for faster response times, and Wazuh and Splunk to analyze log files. Rules are created using the Snorpy tool and added to Snort to monitor for specific attacks like ICMP ping impersonation and authentication attempts. When attacks are attempted, the system successfully detects them and logs the alerts. The integration of these tools provides low-cost intrusion detection capabilities with automated threat identification and faster response compared to existing Snort configurations.
Artificial Intelligence (AI) has rapidly become a critical technology for businesses seeking to improve
efficiency and profitability. One area where AI is proving particularly impactful is in service operations
management, where it is used to create AI-powered service operations (AIServiceOps) that deliver highvalue services to customers. AIServiceOps involve the use of AI to automate and optimize various business
processes, such as customer service, sales, marketing, and supply chain management. The rapid
development of Artificial Intelligence has prompted many changes in the field of Information Technology
(IT) Service Operations. IT Service Operations are driven by AI, i.e., AIServiceOps. AI has empowered
new vitality and addressed many challenges in IT Service Operations. However, there is a literature gap on
the Business Value Impact of Artificial intelligence (AI) Powered IT Service Operations. It can help IT
build optimized business resilience by creating value in complex and ever-changing environments as
product organizations move faster than IT can handle. So, this research paper examines how AIServiceOps
creates business value and sustainability, basically how AIServiceOps makes the IT staff liberation from a
low-level, repetitive workout and traditional IT practices for a continuously optimized process. One of the
research objectives is to compare Traditional IT Service Operations with AIServiceOPs. This paper
provides the basis for how enterprises can evaluate AIServiceOps and consider it a digital transformation
tool. The paper presents a case study of a company that implemented AI-powered service operations
(AIServiceOps) and analyzes the resulting business outcomes. The study shows that AIServiceOps can
significantly improve service delivery, reduce response times, and increase customer satisfaction.
Furthermore, it demonstrates how AIServiceOps can deliver substantial cost savings, such as reducing
labor costs and minimizing downtime.
MEDIATING AND MODERATING FACTORS AFFECTING READINESS TO IOT APPLICATIONS: THE...IJMIT JOURNAL
Although IOT seems to be the upcoming trend, it is still in its infancy; especially in the banking industry.
There is a clear gap in literature, as only few studies identify factors affecting readiness to IOT
applications in banks in general, and almost negligible investigations on mediating and moderating
factors. Accordingly, this research aims to investigate the main factors that affect employees’ readiness to
IOT applications, while highlighting the mediating and moderating factors in the Egyptian banking sector.
The importance of Egypt stems from its high population and steady steps taken towards technology
adoption. 479 valid questionnaires were distributed over HR employees in banks. Data collected was
statistically analysed using Regression and SEM. Results showed a significant impact of ‘Security’,
‘Networking’, ‘Software Development’ and ‘Regulations’ on ‘readiness to IOT applications. Thus, the
readiness acceptance level is high‘Security’ and ‘User Intention’ were proven to mediate the relationship
between research variables and readiness to IOT applications, and only a partial moderation role was
proven for ‘Efficiency’. The study contributes to increasing literature on IOT applications in general, and
fills a gap on the Egyptian banking context in particular. Finally, it provides decision makers at banks with
useful guidelines on how to optimally promote IOT applications among employees.
EFFECTIVELY CONNECT ACQUIRED TECHNOLOGY TO INNOVATION OVER A LONG PERIODIJMIT JOURNAL
IT (Information and Communication Technology) companies are facing the dilemma of decreasing
productivity despite increasing research and development efforts. M&A (Merger and Acquisition) is being
considered as a breakthrough solution. From existing research, it has been pointed out that M&A leads to
the emergence of new innovations. Purpose of this study was to discuss the efficient ways of acquisition and
to resolve the dilemma of productivity decline by clarifying how the technology obtained through M&A
leads to the creation of new innovations. Hypothesis 1 was that the technology acquired through M&A is
utilized for innovation creation, Hypothesis 2 was that the acquired technology is utilized over a long
period of time, and Hypothesis 3 was that a long-term utilization has a positive impact on corporate
performance. The results, using sports prosthetics as a case study and using patents as a proxy variable,
confirmed all the hypotheses set. We have revealed that long-term utilization of technology obtained
through M&A is effective for creating new innovations.
International Journal of Managing Information Technology (IJMIT) ** WJCI IndexedIJMIT JOURNAL
The International Journal of Managing Information Technology (IJMIT) is a quarterly peer-reviewed journal that publishes articles on the strategic application of information technology in organizations from both academic and industry perspectives. The journal focuses on innovative uses of IT to support organizational goals and foster collaboration both within and outside organizations. It covers topics such as education technology, e-government, healthcare IT, mobile systems, and more. Authors are invited to submit original research papers for consideration through the journal's online submission system.
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023)IJMIT JOURNAL
4th International Conference on Cloud, Big Data and IoT (CBIoT 2023) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Cloud, Big Data and IoT. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Cloud, Big Data and IoT.
Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Cloud, Big Data and IoT.
TRANSFORMING SERVICE OPERATIONS WITH AI: A CASE FOR BUSINESS VALUEIJMIT JOURNAL
This document discusses how AI-powered service operations (AIServiceOps) can create business value through digital transformation. It begins with background on digital transformation and how AI is driving changes in IT service operations. It then examines how AIServiceOps can streamline processes, provide insights, and improve customer experience. A case study is presented showing how one company implemented AIServiceOps to significantly reduce response times, increase customer satisfaction, and lower costs. The document argues that AIServiceOps can deliver both quantifiable and flexible benefits while enhancing organizational resilience and sustainability over the long term.
DESIGNING A FRAMEWORK FOR ENHANCING THE ONLINE KNOWLEDGE-SHARING BEHAVIOR OF ...IJMIT JOURNAL
The main objective of this paper is to identify the factors that influence academic staff's digital knowledgesharing behaviors in Ethiopian higher education. A structural equation model was used to validate the
research framework using survey data from 210 respondents. The collected data has been analyzed using
Smart PLS software. The results of the study show that trust, self-motivation, and altruism are positively
related to attitude. Contrary to our expectations, knowledge technology negatively affects attitude.
However, reward systems and empowerment by leaders are significantly associated with knowledgesharing intentions.Knowledge-sharing intention, in turn, was significantly related to digital knowledgesharing behavior. The contributions of this study are twofold. The framework may serve as a roadmap for
future researchers and managers considering their strategy to enhance digital knowledge sharing in HEI.
The findings will benefit academic staff and university administrations.The study will also help academic
staff enhance their knowledge-sharing practices.
BUILDING RELIABLE CLOUD SYSTEMS THROUGH CHAOS ENGINEERINGIJMIT JOURNAL
Cloud computing systems need to be reliable so that they can be accessed and used for computing at any
given point in time. The complex nature of cloud systems is the motivation to conduct research in novel
ways of ensuring that cloud systems are built with reliability in mind. In building cloud systems, it is
expected that the cloud system will be able to deal with high demands and unexpected events that affect the
reliability and performance of the system.
In this paper, chaos engineering is considered a heuristic method that can be used to build reliable cloud
systems. Chaos engineering is aimed at exposing weaknesses in systems that are in production. Chaos
engineering will help identify system weaknesses and strengths when a system is exposed to unexpected
knocks and shocks while it is in production.
Chaos engineering allows system developers and administrators to get insights into how the cloud system
will behave when it is exposed to unexpected occurrences.
A REVIEW OF STOCK TREND PREDICTION WITH COMBINATION OF EFFECTIVE MULTI TECHNI...IJMIT JOURNAL
It is important for investors to understand stock trends and market conditions before trading stocks. Both
these capabilities are very important for an investor in order to obtain maximized profit and minimized
losses. Without this capability, investors will suffer losses due to their ignorance regarding stock trends
and market conditions. Technical analysis helps to understand stock prices behavior with regards to past
trends, the signals given by indicators and the major turning points of the market price. This paper reviews
the stock trend predictions with a combination of the effective multi technical indicator strategy to increase
investment performance by taking into account the global performance and the proposed combination of
effective multi technical indicator strategy model.
NETWORK MEDIA ATTENTION AND GREEN TECHNOLOGY INNOVATIONIJMIT JOURNAL
This paper will provide a novel empirical study for the relationship between network media attention and
green technology innovation and examine how network media attention can ease financing constraints. It
collected data from listed companies in China's heavy pollution industry and performed rigorous
regression analysis, in order to innovatively explore the environmental governance functions of the media.
It found that network media attention significantly promotes green technology innovation. By analyzing the
inner mechanism further, it found that network media attention can promote green innovation by easing
financing constraints. Besides, network media attention has a significant positive impact on green invention
patents while not affecting green utility model patents.
INCLUSIVE ENTREPRENEURSHIP IN HANDLING COMPETING INSTITUTIONAL LOGICS FOR DHI...IJMIT JOURNAL
Information System (IS) research advocates employing collaborative and loose coupling strategies to address contradictory issues to address diversified actors’ interests than the prescriptive and unilateral Information Technology (IT) governance mechanisms’, yet it is rarely depicting how managers employ these strategies in Health Information System (HIS) implementation, particularly in a resource-constrained setting where IS implementation activities have highly relied on multiple international organizations resources. This study explored how managers in resource-constrained settings employ collaborative IT governance mechanisms in the case of District Health Information System 2 (DHIS2) adoption with an interpretative case study approach and the institutional logic concept. The institutional logic concept was used to identify the major actors’ logics underpinning the DHIS2 adoption. The study depicted the importance of high-level officials' distance from the dominant systemic logic to consider new alternative, and to employ inclusive IT governance mechanisms which separated resource from the system that facilitated stakeholders’ collaboration in DHIS2 adoption based on their capacity and interest.
DEEP LEARNING APPROACH FOR EVENT MONITORING SYSTEMIJMIT JOURNAL
With an increasing number of extreme events and complexity, more alarms are being used to monitor
control rooms. Operators in the control rooms need to monitor and analyze these alarms to take suitable
actions to ensure the system’s stability and security. Security is the biggest concern in the modern world. It
is important to have a rigid surveillance that should guarantee protection from any sought of hazard.
Considering security, Closed Circuit TV (CCTV) cameras are being utilized for reconnaissance, but these
CCTV cameras require a person for supervision. As a human being, there can be a possibility to be tired
off in supervision at any point of time. So, we need a system to detect automatically. Thus, we came up with
a solution using YOLO V5. We have taken a data set and used robo-flow framework to enhance the existing
images into numerous variations where it will create a copy of grey scale image, a copy of its rotation and
a copy of its blurred version which will be used to get an enlarged data set. This work mainly focuses on
providing a secure environment using CCTV live footage as a source to detect the weapons. Using YOLO
algorithm, it divides an image from the video into grid system and each grid detects an object within itself
MULTIMODAL COURSE DESIGN AND IMPLEMENTATION USING LEML AND LMS FOR INSTRUCTIO...IJMIT JOURNAL
The document discusses course delivery modalities including face-to-face, online asynchronous, online synchronous, hybrid, and HyFlex. It investigates the design and implementation of courses using the Learning Environment Modeling Language (LEML) for different delivery environments. The authors describe their experience delivering courses at Southern University and A&M College and Baton Rouge Community College. They aim to answer questions about the course delivery methods used by their institutions and how to validate guidelines and ensure student learning outcomes.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
KuberTENes Birthday Bash Guadalajara - K8sGPT first impressionsVictor Morales
K8sGPT is a tool that analyzes and diagnoses Kubernetes clusters. This presentation was used to share the requirements and dependencies to deploy K8sGPT in a local environment.
artificial intelligence and data science contents.pptxGauravCar
What is artificial intelligence? Artificial intelligence is the ability of a computer or computer-controlled robot to perform tasks that are commonly associated with the intellectual processes characteristic of humans, such as the ability to reason.
› ...
Artificial intelligence (AI) | Definitio
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
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Automatic recommendation for online users using web usage mining
1. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
DOI : 10.5121/ijmit.2010.2303 33
Automatic Recommendation for Online Users
Using Web Usage Mining
Ms.Dipa Dixit 1
Mr Jayant Gadge2
Lecturer 1
Asst.Professor2
Fr CRIT , Vashi Navi Mumbai1
Thadomal Shahani Engineering College,Bandra 2
Email:dipa.pathak@gmail.com 1
jayantrg@hotmail.com2
ABSTRACT
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.
KEYWORDS
Data Mining, Web Usage mining, Web Intelligence, Personalization, Clustering, Classification
1. INTRODUCTION
With the explosive growth of knowledge available on World Wide Web, which lacks an
integrated structure or schema, it becomes much more difficult for users to access relevant
information efficiently. Meanwhile, the substantial increase in the number of websites presents a
challenging task for web masters to organize the contents of websites to cater to the need of
user’s. Analyzing and modeling web navigation behavior is helpful in understanding demands
of online users. Following that, the analyzed results can be seen as knowledge to be used in
intelligent online applications, refining website maps, and web based personalization system
and improving searching accuracy when seeking information. Nevertheless, an online
navigation behavior grows each passing day, thus extracting information intelligently from it is
a difficult issue. Web Usage Mining (WUM) is process of extracting knowledge from Web
user’s access data, by exploiting Data Mining technologies. It can be used for different purposes
such as personalization, system improvement and site modification. A typical application of
Web Usage Mining is represented by so called recommender system. The main goal of the
recommender system is to improve Web site usability. Typically, the Web usage mining
prediction process is structured according to two components performed online and off-line with
respect to Web server activity. Offline component builds the knowledge base by analyzing
historical data, such as server access log file or web logs which are captured from the server,
2. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
34
then these web logs are used in the online component for capturing the intuition list of the user
so as to recommend page views to the user whenever he / she comes online for the next time.
In our paper, we present architecture for capturing recommendations in the form of intuition list
for user. Intuition List consists of list of pages visited by user as well as list of pages visited by
other user of having similar usage profile. The results represent that improved accuracy of
recommendations. The rest of this paper is organized as follows: In section 2, we review some
researches that advance in understanding of recommendation systems using web usage mining.
Section 3 describes the block diagram and implementation for the Recommendation System.
Results and discussion are shown in section 4. Finally, section 5 summarizes the paper and
introduces future work.
2. RELATED WORK
Recently, several Web Usage Mining systems have been proposed to predicting user navigation
behavior and their preferences. In the following we review some of the most significant WUM
systems and architecture that can be compared with our system Analog[8] is one of the first
WUM systems .It is structured according to an offline and an online component. The off-line
component build session clusters by analyzing past user activity recorded in server log files.
Then the online component builds active user sessions which are then classified according to
generated model. The classification allows to identify pages related to the ones in the active
session and to return the requested page with a list of suggestions. This approach has several
limitations, related to scalability. Nevertheless, architectural solution introduced was
maintained in several other more projects. In Mobasher et al[1] present Web personalizer a
system which provides dynamic recommendations, as a list of hypertext links, to users. The
analysis is based on anonymous usage data combined with the structure formed by hyperlinks of
the site. Data mining techniques (i.e. clustering, sequence pattern discovery and association
rules) are used in preprocessing phase in order to obtain aggregate usage profiles. In this phase
Web server logs are converted into clusters of visited pages, and cluster made up of set of pages
with common usage characteristics. The online phase considers active user session in order to
find matches among user’s activities and discovered usage profiles. Matching entries are used to
compute a set of recommendations which will be inserted into last requested page as list of
hypertext links. Web Personalizer is a good example of two tier architecture for Personalization
Systems. Baraglia and Palmerini proposed a WUM system called SUGGEST, that provide
useful information to make easier the web user navigation and to optimize the web server
performance [6, 7]. SUGGEST adopts a two level architecture composed of offline creation of
historical knowledge and online engine that understands user’s behavior. As the request arrives
at this system module it incrementally updates a graph representation of web site based on the
active user sessions and classifies the active session using a graph partitioning algorithm.
Potential limitation of this architecture might be:
a) the memory required to store Web server pages in quadratic in the number of pages .This
might be severe limitation in larger sites made of million pages;
b)it does not permit us to manage web sites made up of pages dynamically generated. All of
these works attempt to find the architecture and algorithm to improve accuracy of personalized
recommendation, but accuracy still does not meet satisfaction. In our work we advance
3. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
35
architecture and propose a classification approach using visited and unvisited pages of user in
the architecture for improving accuracy of recommendation for users.
3. BLOCK DIAGRAM AND IMPLEMENTATION OF
RECOMMENDATION SYSTEM
Block diagram of the Recommendation System is given below.
front-end phase
Front-end phase
Web logs
Back-end phase
3.1 Implementation of Recommendation System
Implementation of System is done in two phases, Back-end and Front-end phase.
3.1.1Back-end phase :
Steps involved in back-end phase are explained below.
Step 1: Data sets consisting of 5000 web log records are collected from De Paul University
website. Web log is an unprocessed text file which is recorded from the IIS Web Server. Web
log consist of 17 attributes with the data values in the form of records.
Fragment of web log from IIS web server is shown below:
Fields: date time c-ip cs-username s-sitename s-computername s-ip s-port cs-method cs-uri-
stem cs-uri-query sc-status time-taken cs-version cs-host cs(User-Agent) cs(Referer) .
Data pre-processing
User Navigation
Mining
List of unvisited
pages
Live Session
Window
Navigation profile o/p
Classification
Algorithm
Intelligent
Agent Engine
Knowledge Base
Captured List
User
Figure1: Architecture of Recommendation System for Online Users
4. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
36
Step2: Generally, several preprocessing tasks need to be done before performing web mining
algorithms on the Web server logs. Data preprocessing, a web usage mining model aims to
reformat the original web logs to identify user’s access session. The Web server usually
registers all users’ access activities of the website as Web server log. Due to different server
setting parameters, there are many types of web logs, but typically the log files share the same
basic information, such as: client IP address, request time, requested URL, HTTP status code,
referrer, etc.
Data pre-processing is done using following steps.
1. Data Cleansing: Irrelevant records are eliminated during data cleansing. Since target of
web usage mining is to get traversal pattern, following two kinds of records are unnecessary
and should be removed :
a.The records having filenames suffixes of GIF, JPEG, CSS.
b. By examining the status field of every record in the web log, the record with status code
over 299 and below 200 are removed.
2. User and Session Identification: The task of user and session identification is to find out the
different user sessions from the original web access log. A referrer-based method is used for
identifying sessions. The different IP addresses distinguish different users.
a.If the IP addresses are same, different browsers and operation system’s indicate different
users which can be found by client IP address and user agent who gives information of
user’s browsers and operating system.
b.If all of the IP address, browsers and operating systems are same, the referrer information
should be taken into account. The ReferURI is checked, new user’s session is identified if
the URL in the ReferURI is ‘-’ that is field hasn't been accessed previously, or there is a
large interval of more than 30 minutes between the accessing time of this record.
3. Content Retrieval: Content Retrieval retrieves content from users query request i.e.
cs_uri_query.
Eg:Query:http/1www.cs.depaul.edu/courses/syllabus.asp?course=323-21-
603&q=3&y=2002&id=671.
Retrieve the content like /courses/syllabus.asp which helps in fast searching of unvisited pages
i.e; pages of other user’s which are similar to user’s interest.
4. Path Completion: Path Completion should be used acquiring the complete user access path.
The incomplete access path of every user session is recognized based on user session
identification. If in a start of user session, Referrer as well URI has data value, delete value of
Figure 2: Block diagram for Pre-processing
Data
Cleansing
User
Identification
Session
Identification
Content
Retrieval
Path
Completion
5. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
37
Referrer by adding ‘-‘. Web log preprocessing helps in removal of unwanted records from the
log file and also reduces the size of original file by 40-50%.
Step 3: Generation of Page Id: Page Id is sequence generated numbers like p1, p2, p3….which
are created for pages/page views.
Step 4: User Navigation Mining: Web pages accessed are modeled as undirected graph G=
(V, E). The set V of vertices contains the identifiers of the different pages hosted on the Web
server and E is edges of the graph.
a. Undirected graph is created for a single user session using Hash Map.
b. Hash Map data structure stores the referrer–URI pair and their corresponding weights.
c. Weight of edges given by 1, only if link between page and referrer exist, else weight is 0.
d. Weights of pages are frequency connectivity of pages in graph. i.e.
Weight of pages (W) = Frequency (F) of referrer–URI pair (occurrence in user session)
e. Apply Depth First Search Algorithm (DFS) on graph and obtain all possible navigation
patterns.
f. Path length of pattern is calculated by considering the total weight of the edges in a graph.
g. If navigation pattern weight /path length is less than three, then pattern is not considered for
analysis. (Minpathlength = 3).
Hence, clusters of patterns for user sessions are obtained and fed into Knowledge base for
further analysis.
3.1.2 Front End Phase
Step 1: Longest Common Subsequence Algorithm:
a. Capture the Live Session Window (LSW) for a user dynamically [5].
b. Intelligent Agent Engine compares LSW of a user with patterns of same user in
knowledge base.
c. Check for the longest pattern or the largest path length of a pattern from knowledge base.
d. Compare both the sequences, longest common subsequence is obtained.
e. Consider the pages which are not present in subsequence, these pages are the Intuition
pages for the user as they are visited by user most frequently.
f. Recommendation list is given in the form of URI (content) as well as IDs of pages.
Hence, recommendation/intuition list as compared to user’s historical pattern are captured.
Step2: Searching of Unvisited pages (as compared to others user’s pattern)
a. Unvisited pages when compared to other user’s pattern are searched using searching
algorithm.
b. Searching algorithm compares the live session window of user and patterns of other
user’s present in Knowledge Base.
c. Best possible pattern is achieved by considering longest path length (weight).
6. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
38
d. Subsequent pages are removed and the Unvisited Page List is created in the form of
URI and IDs.
Recommendation List as compared to others user’s pattern (unvisited pages) are captured and
added to original recommendation list
4. RESULTS AND DISCUSSIONS
Step wise results are shown below for 5000 web log records from De Paul University dataset
(CTI dataset).
Step 1: Collection of web logs which are in raw or unprocessed form.17 attributes are shown
below:
2002-04-01 00:00:10 1cust62.tnt40.chi5.da.uu.net - w3svc3 bach bach.cs.depaul.edu
80 get /courses/syllabus.asp course=323-21-603&q=3&y=2002&id=671 200 156
http/1.1 www.cs.depaul.edu
mozilla/4.0+(compatible;+msie+5.5;+windows+98;+win+9x+4.90;+msn+6.1;+msnbm
sft;+msnmen-us;+msnc21) http://www.cs.depaul.edu/courses/syllabilist.asp
depaul.edu/courses/syllabilist.asp
2002-04-01 00:00:26 ac9781e5.ipt.aol.com - w3svc3 bach bach.cs.depaul.edu 80 get
/advising/default.asp - 200 16 http/1.1 www.cs.depaul.edu
mozilla/4.0+(compatible;+msie+5.0;+msnia;+windows+98;+digext)
http://www.cs.depaul.edu/news/news.asp?theid=573
Step 2: Preprocessing is done for 5000 web log records. Cleansing, User and Session
Identification, Content Retrieval and Path Completion applied on records.
Preprocessing of 5000 records was done in 14secs.
User Sessions were identified for 200 users.
Thus, processed records for a user id 9 in user’s sessionized form are as shown below.
Figure 3: Processed file with required attributes for user id 9
7. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
39
Step 3: Page Id is generated for the URI/pages/page view accessed by user
Figure 4: List of Page id and corresponding pages/uri
Step 4: In User Navigation Mining undirected graphs are created and clusters of all possible
patterns are generated for a user session
Figure 5: Cluster of patterns for user id=9
When Clusters of navigation Patterns were compared to original user navigation patterns most
of pages views (99%) were covered in the clusters. Very few i.e; 1% of outliers were obtained.
Step 5: Considered Two cases of Live Session Window (LSW) of size 2 and varying patterns
ie; one pattern having few page views and other having more number of page views these cases
are shown in Step 6. Live Session Window (LSW) consists of 10% of pages of actual page
8. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
40
views of user (Originalnp). Classification is done by applying Longest Common Subsequence
algorithm on LSW and rest of pages present in original page view list of user. Thus the intuition
list obtained is from the history of user’s navigation pattern.
Step 6: Apply Searching algorithm to get the intuition list of the user’s whose usage pattern is
same as the user. Thus, both the lists are combined into the Final Recommendation List of the
user. Finally, Accuracy is calculated for the final recommendation list.
Accuracy measures the degree to which the recommendation system produces accurate
recommendations. It is given by
|),(|
|)l(),(| n
lswLSP
OriginalswLSP
np
pnp I
(1)
lsw = Live Session Window
),( lswLSP np -Navigation pattern in captured list recommended by engine.
pOrigina nl -Original page views / pattern of user.
Case 1: When LSW of size 2 was considered for user id 9, having 13 page views.
Recommendation List obtained had accuracy of 66.6%, which is shown below.
Figure 6: recommendation list for user id=9 and accuracy is 66.6%
9. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
41
Case 2: When LSW of size 2 was considered for user id 89, having 17 page views,
recommendation list obtained had accuracy 85.71%, as shown below.
Figure 7: recommendation list for user id=89 and accuracy is 85.71%
Thus, from above cases we can prove that accuracy of the recommendation list increases if the
number of page views is more in the user navigation pattern.
5. CONCLUSION
In this paper, we propose a two tier architecture for capturing user’s intuition in the form of
recommendation list containing list of pages visited by user and also list of pages visited by
other user’s having similar usage profile. The practical implementation of proposed architecture
and algorithms shows that accuracy of user intuition capturing improves up to 85 percent for
Live Session Window size of two, if numbers of page views having maximum weights are more
in the navigation patterns of the user. In the future, we would like to substantially improve the
accuracy and coverage parameter by trying to increase the Live Session Window (LSW) size
and considering more number of log records.
10. International Journal of Managing Information Technology (IJMIT) Vol.2, No.3, August 2010
42
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