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.
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.
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.
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.
Web Personalization Using Usage Based Clustering
In today’s internet environment it is more difficult to
access the relevant information from the web. Because
www is a vast data warehouse of web pages and links .On
internet huge amount of information is available which
are approximately 1 millions of pages are added day to
day. To get the “right” information from such warehouse
to the user and to avoid website exploration web
personalization get needed. It is the life blood of web
usages mining and e-learning process to improve the
system and its design as per the user’s interest. It acts as
a tool to avoid the content over loading on websites for
effective web navigation. Here we present web
personalization which introduces web mining that is
application of a data mining.
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.
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.
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.
Web Personalization Using Usage Based Clustering
In today’s internet environment it is more difficult to
access the relevant information from the web. Because
www is a vast data warehouse of web pages and links .On
internet huge amount of information is available which
are approximately 1 millions of pages are added day to
day. To get the “right” information from such warehouse
to the user and to avoid website exploration web
personalization get needed. It is the life blood of web
usages mining and e-learning process to improve the
system and its design as per the user’s interest. It acts as
a tool to avoid the content over loading on websites for
effective web navigation. Here we present web
personalization which introduces web mining that is
application of a data mining.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
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.
Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.
Data mining is the process of finding correlation or patterns among fields in large relational databases.
The process of extracting valid, previously unknown, comprehensible , and actionable information from large databases and using it to make crucial business decisions.
Web personalization using clustering of web usage dataijfcstjournal
The exponential growth in the number and the complexity of information resources and services on the Web
has made log data an indispensable resource to characterize the users for Web-based environment. It
creates information of related web data in the form of hierarchy structure through approximation. This
hierarchy structure can be used as the input for a variety of data mining tasks such as clustering,
association rule mining, sequence mining etc.
In this paper, we present an approach for personalizing web user environment dynamically when he
interacting with web by clustering of web usage data using concept hierarchy. The system is inferred from
the web server’s access logs by means of data and web usage mining techniques to extract the information
about users. The extracted knowledge is used for the purpose of offering a personalized view of the
services to users.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
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.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Web Usage Mining: A Survey on User's Navigation Pattern from Web Logsijsrd.com
With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
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.
Data mining refers to the process of analysing the data from different perspectives and summarizing it into useful information.
Data mining software is one of the number of tools used for analysing data. It allows users to analyse from many different dimensions and angles, categorize it, and summarize the relationship identified.
Data mining is about technique for finding and describing Structural Patterns in data.
Data mining is the process of finding correlation or patterns among fields in large relational databases.
The process of extracting valid, previously unknown, comprehensible , and actionable information from large databases and using it to make crucial business decisions.
Web personalization using clustering of web usage dataijfcstjournal
The exponential growth in the number and the complexity of information resources and services on the Web
has made log data an indispensable resource to characterize the users for Web-based environment. It
creates information of related web data in the form of hierarchy structure through approximation. This
hierarchy structure can be used as the input for a variety of data mining tasks such as clustering,
association rule mining, sequence mining etc.
In this paper, we present an approach for personalizing web user environment dynamically when he
interacting with web by clustering of web usage data using concept hierarchy. The system is inferred from
the web server’s access logs by means of data and web usage mining techniques to extract the information
about users. The extracted knowledge is used for the purpose of offering a personalized view of the
services to users.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
IJRET : International Journal of Research in Engineering and TechnologyImprov...eSAT Publishing House
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.
Implementation of Intelligent Web Server Monitoringiosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
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
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.
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.
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.
Enhanced Enterprise Intelligence with your personal AI Data Copilot.pdfGetInData
Recently we have observed the rise of open-source Large Language Models (LLMs) that are community-driven or developed by the AI market leaders, such as Meta (Llama3), Databricks (DBRX) and Snowflake (Arctic). On the other hand, there is a growth in interest in specialized, carefully fine-tuned yet relatively small models that can efficiently assist programmers in day-to-day tasks. Finally, Retrieval-Augmented Generation (RAG) architectures have gained a lot of traction as the preferred approach for LLMs context and prompt augmentation for building conversational SQL data copilots, code copilots and chatbots.
In this presentation, we will show how we built upon these three concepts a robust Data Copilot that can help to democratize access to company data assets and boost performance of everyone working with data platforms.
Why do we need yet another (open-source ) Copilot?
How can we build one?
Architecture and evaluation
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Unleashing the Power of Data_ Choosing a Trusted Analytics Platform.pdfEnterprise Wired
In this guide, we'll explore the key considerations and features to look for when choosing a Trusted analytics platform that meets your organization's needs and delivers actionable intelligence you can trust.
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
1. International Journal of Computer Applications (0975 – 8887)
Volume 87 – No.3, February 2014
22
Algorithm for Tracing Visitors’ On-Line Behaviors for
Effective Web Usage Mining
S. Umamaheswari
Research Scholar
SCSVMV University
Kanchipuram, India
S. K. Srivatsa
Senior Professor
St.Joseph College of Engineering,
Chennai, India
ABSTRACT
User behavior identification is an important task in web usage
mining. Web usage mining is also called as web log mining.
The web logs are mainly used to identify the user behavior.
There are so many pattern mining methods which enable this
user behavior identification. The preprocessing techniques
will maximize the accurate and quality of pattern mining
methodologies. In existing algorithms, the preprocessing
concepts are applied to calculate the unique user’s count, to
minimize the log file size and to identify the sessions. The
newly proposed algorithm is Visitors’ Online Behavior
(VOB) which identifies user behavior, creates user cluster and
page cluster, and tells the most popular web page and least
popular web page. This paper brings into discussion about the
basic concepts of web mining, web usage mining, general data
preprocessing, how to preprocess the web data, what are the
various existing preprocessing techniques and the proposed
VOB algorithm.
Keywords
Data preprocessing methods, Web mining, Web usage mining,
Web usage data, Web log.
1. INTRODUCTION
World wide web is a very large, widely distributed, global
information service centre to facilitate the services such as
news, advertisements, consumer information, financial
management, education, government, e-commerce, etc. It
consists of hyper-link information, access and usage
information. World wide web gives enough number of rich
sources of data for data mining .Web Mining is one of the
data mining technique used to automatically discover and
extract information from web documents or services. This
refers a process by which we can discover useful information
from the world wide web and it’s usage patterns. Here the
data objects are linked together for interactive access. The
subtasks of web mining are resource finding, information
selection and preprocessing, generalization and analysis.
Resource finding refers the task of retrieving intended web
documents. Information selection and preprocessing is an
automatic selection and preprocessing of particular
information from retrieved web resources. Also the
generalization represents an automatic discovery of patterns in
web sites and analysis is the validation and interpretation of
mined patterns. Generally data mining techniques are used to
make the web more useful and more profitable for some and
to increase the efficiency of our interaction with the
web.Some of the data mining techniques are association
rules, sequential patterns, classification, clustering and outlier
discovery. Nowadays these techniques and concepts have
employed in many applications to the web like e-commerce,
information retrieval and network management.
1.1 Why Mine the Web?
There are enormous wealth of information on web such as
financial information like stock quotes, book/CD/video stores,
restaurant information and car prices. Even though it has
many sort of information, the web poses great challenges for
effective resources and knowledge discovery. The web seems
to be too huge for effective data warehousing and mining.
Also the complexity of web pages is far greater than that of
any old text documents. Only a small portion of the
information on the web is truly relevant [4].It is possible to
get lots of data on user access patterns and also possible to
mine interesting nuggets of information. The process of
searching the web is illustrated in the following “Fig. 1”.
Web has recently become a powerful platform for retrieving
information and discovering knowledge from web data. The
idea of discovering useful patterns in data may have many
names such as data mining, knowledge extraction,
information discovery, information harvesting, data
archeology, and data pattern processing[12].
1.2 Web Mining Applications
Web mining applications are listed such as to target potential
customers for electronic commerce, to enhance the quality
and delivery of internet information services to the end user,
to improve the web server program’s performance, to identify
the potential prime advertisement locations, to facilitate
adaptive sites, to improve site design, to do fraud detection
and to predict the user’s actions.
1.3 Web Mining Issues
Nowadays the web became so popular and used by many
categories of people which includes school students and the
business men also. The number of users who are employing
Content
aggregators
Google
msn
Yahoo!
W
E
B
C
O
N
S
U
M
E
R
S
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the web is increasing at exponential speed [3].On the web,
many different types of data such as images, text, audio/video,
XML and HTML are used. Web datasets can be very large. It
is in the range of tens to hundreds of tera bytes. So it cannot
mine on a single server. There is a need of large forms of
servers.
2. WEB USAGE MINING
Web usage mining is a category of web mining technique to
discover interesting usage patterns from the secondary data
derived from the interactions of the users while surfing the
web.
The web pages contain information. Here actually the links
are ‘roads’. It tells how the people navigate the internet. The
information on navigation paths is available in log files. Logs
can be mined from a client or a server perspective .It is aimed
to discover user ‘navigation patterns’ from web data and to
predict the user’s behavior while the user interacts with the
web. Also it helps to improve large collection of resources
[5].The web usage mining techniques are to construct
multidimensional view on the weblog database, to perform
data mining on web log records and also to conduct studies
for analyzing the system performance, etc. Some of the
frequently used techniques are such as data collection, data
preparation and data cleaning. The web usage mining process
is given in the following “Fig. 2”.
2.1 What is the need for tracing visitors’
on-line behaviors in web usage mining
It must to trace the visitors’ on-line behaviors for website
usage analysis. Actually it is an analysis to get knowledge
about how visitors use website which could provide
guidelines to web site reorganization and helps to prevent
disorientation. It also helps to the designers in placing the
important information where the visitors look for it. It has to
be done for pre-fetching and caching web pages. Also it
provides adaptive website (personalization). This is
represented in the figure “Fig 3.” given below.
Figure 3. Website usage analysis
Many organizations have been supported by the analysis of
user’s browsing patterns for the purpose of giving
personalized recommendations of web pages. Generally the
usage-based personalized recommendation gives solution to
many of the problems occurred in the web [13, 14, 15].It has
created an interest between the researchers to do research. The
recommendation systems listen the information overload by
suggesting pages that fullfills the user’s requirement. In recent
days, the web usage mining has great potential and frequently
employed for the tasks like web personalization, web pages
pre-fetching and website reorganization, etc [16]. Data
sources for web usage mining are obtained in three ways [12].
In server level, the server keeps the client request details. At
the client level, the client itself forwards data about user’s
behavior to a database .It can be accomplished by using either
an ad-hoc browsing application or through client side
application which runs on the standard browsers. In the proxy
level, the proxy side maintains user behavior information.
Even though the web data is taken from many users on
various web sites, only the users whose web clients pass
through the proxy.
2.2 Web usage data
Generally the web pages, intra page structures, inter page
structures and usage data are the input used in web usage
mining. Other forms of web data resides as profiles,
registration information and cookies. Web usage data is
referred as the collective data about how a user utilizes a web
site through his mouse and keyboard. This data can also be
available in form of web server logs, referral logs,
registration-files and index server logs and cookies.
2.3 Web log
The aim of web log file is to create user profile by allowing
their browsing similarities with previous users. Before the
data mining process, it is required to clean, condense and
transform the raw data of weblog before performing data
mining. Weblog information can be integrated with web
content and web structure mining to help webpage ranking
and web document classification. The interaction details of
users with website are recorded automatically in web servers
as the form of weblogs [2]. Weblogs are kept as in form of
line of text in web server, proxy server and browser
[8].Various forms of logs are server access logs, server
referrer logs, agent logs, client-side cookies, user profiles,
search engine logs and database logs. These are considered as
input for knowing the end user behavior in web usage mining.
Log files are those files that list the actions that have been
Web
serv
er
Log
Site
data
Data
Prepar
ation
Data
cleani
ng
Min
ing
Usage
pattern
s
PersonalizationPrefetching
web pages
Website
Reorganization
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24
occurred [18].Log files hold many parameters which have
employed in recognizing user browsing patterns. Some of the
parameters are user name, visiting path traversed, timestamp,
page last visited, success rate, user agent, URL and request
type [17].
2.4 Transfer / Access Log
The information on user’s request from their web browsers is
stored in transfer/access log.
Table 1. Transfer/Access Log
Time Date
Host
name
File
requested
Amount
of data
Transferred
Status
of
report
2.5 Referrer Log
The recorded two fields of referrer log are URL and referrer
URL.
Table 2. Referrer Log
URL Referrer URL
2.6 Error Log
The list of errors and requests which have failed are collected
in error log. Not only for the page which holds links to a file
that does not exist, but also for the user who is not permitted
to access a particular page, the user request may fail. It is
depicted in the following “Fig 4.”
Figure 4. Error Log
When cookies are used by the websites, the information will
be in the cookies field of log file. Web traffic analysis
software employs the cookies to track the repeat visitors.
3.DATA PREPROCESSING METHODS
The raw data may include noise, missing values, and
inconsistency mostly. The data mining results have been
affected by the data quality. So it must to preprocess the data
for the purpose of increasing the quality and efficiency. The
process of preprocessing contains data preparation and
transformation of the initial dataset. The preprocessing
methods are categorized such as data cleaning, data
integration, data transformation, data reduction and data
discretization[4].
3.1Data cleaning
Data cleaning is an essential requirement of preprocessing
methodologies. It is done for duplicate tuples. It will remove
the unwanted data and shapes the required data by filling in
missing values, smoothing noisy data, identifying or removing
outliers and resolving inconsistencies. Always the dirty data
can make confusion while processing it in the mining process.
3.2 Data integration
Many categories of databases, data cubes or files have been
collected and integrated together in this step.
3.3 Data transformation
It actually pointed by the process of normalization and
aggregation.
3.4. Data reduction
It leads to the reduced representation based on the volume of
data collected and processed. But it gives the same or similar
analytical results.
3.5. Data discretization
It is a section in the data reduction step. This is done only for
the case of numerical data not for all types of data.
4. PREPROCESSING OF WEB USAGE
DATA
Generally in the web usage mining, the preprocessing [9] is
considered as an essential task and treated as an idea to reach
the goal. As it was suggested in referred paper [1], the
intelligent system web usage preprocessor splits the human
and search engine accesses before using the preprocessing
techniques. In the recent days, it is not possible to get good
quality data. Also there is no better result for mining the
quality data. But the quality decisions have been taken
depending upon the quality of data. The duplicate or
missing data may create incorrect or even misleading
statistics. Also the data warehouse requires consistent
integration of quality data. Moreover the data extraction,
cleaning, and transformation take the maximum of the
work in building a data warehouse. It is depicted in the
following “Fig 5.”
4.1 Data cleaning
Data collection [6] is the initial step in weblog preprocessing.
After collecting the data, irrelevant records are removed in the
data cleaning process. Data cleaning [10] refers a process of
eliminating the noisy and irrelevant data which are disturbing
the process of mining the knowledge through weblogs.
4.2 User and Session Identification
From the web access log, different user sessions can be
identified by user as well as session identification. Session
identification [7] is the process of dividing the individual user
access logs into sessions. To identify the various sessions, a
referrer based method is used.
4 .3 Path Completion
This is done in order to acquire the entire user access path.
The incomplete access path of every user session is
recognized based on user session identification. In the start
of user session, the referrer has data values and delete this
value of referrer by adding ‘_’.Also the web log preprocessing
supports the unwanted click stream removal from log file and
to minimize by the original file size 40-50%.
SERVER CLIENT
Request
Reply
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Volume 87 – No.3, February 2014
25
5. AN OVERVIEW OF EXISTING
METHODOLOGIES
This research paper [2] studies and presents several data
preparation techniques of access stream even before the
mining process can be started. These are used to improve the
performance of the data preprocessing, to identify the unique
sessions and unique users. The methods proposed will help to
discover meaningful pattern and relationships from the access
stream of the user. These are proved to be valid and useful by
various research tests. Yang Bin et al. in [19] used negative
association rules in discovery of web visitor’s patterns.
Negative association rules have been deployed to solve the
deficiencies in which positive rules are referred to. It is known
that the data preprocessing is an essential process for effective
mining process. In paper [9], a novel pre-processing technique
is proposed by removing local and global noise and web
robots. Anonymous microsoft web dataset and MSNBC.com
anonymous web dataset are used for estimating this
preprocessing technique.
The paper [1] describes the effective and complete
preprocessing of access stream before actual mining process
can be performed. The log file collected from different
sources undergoes different preprocessing phases to make
actionable data source. It will help to automatic discovery of
meaningful pattern and relationships from access stream of
user. Swarm based web session clustering helps in many ways
to manage the web resources effectively such as web
personalization, schema modification, website modification
and web server performance. In this paper [2], they proposed
a framework for web session clustering at preprocessing level
of web usage mining. The framework will cover the data
preprocessing steps to prepare the weblog data and convert
the categorical weblog data into numerical data. A session
vector is obtained, so that appropriate similarity and swarm
optimization could be applied to cluster the weblog data. The
hierarchical cluster based approach will enhance the existing
web session techniques for more structured information about
the user sessions. The paper [6] introduces an extensive
research framework which is capable of preprocessing web
log data completely and efficiently. The learning algorithm of
proposed research framework separates human user and
search engine accesses intelligently, with less time. In order to
create suitable target data, the further essential tasks of pre-
processing like data cleaning, user identification, session
identification and path completion are designed collectively.
The framework reduces the error rate and improves significant
learning performance of the algorithm. The work ensures the
goodness of split by using popular measures like entropy and
gini index.
In UILP, data cleaning method is used to remove the noisy
and irrelevant information from the weblog. This is one of the
features in identifying the user level of interest. The second
feature used is based on site topology and cookies. Frequency
value, session identification, path completion are also
identified using this UILP algorithm [11]. In UILP
(i) During data cleaning process, explicit image and
multimedia requests from users are considered; those requests
are not removed from weblogs.
(ii) Users are identified based on site topology and cookies.
(iii) Session time is calculated based on the time spent on each
website by a particular user.
(iv) Frequency value is calculated based on the number of
web pages visited by the user on particular website.
Here the site topology is used to identify the user and for
completing the missing path .To label the session, the time
duration is calculated between two nearby website visited by
the particular user. It is calculated each and every time when a
user switches from one website to another and the amount of
time spent in each website.
6. PROPOSED METHODOLOGY
The proposed method tells user behavior and it creates user
cluster and site cluster. Also it gives the information like what
sites are the most and least popular, which website is most
commonly used by visitors and from what search engine are
visitors coming frequently. In this method, if IP address is
unique then similar user cluster is created; If IP address is
same and user name is not unique, agent log, operating system
and browser are different then distinguish user cluster is
created.
Data cleaning
User/session
identification
Page view
identification
Path
completion
Server
session
file
Episode
identification
Episode
file
Raw
usage
data
Usage
statistics
Site
structure
and
content
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26
A. Steps followed
1. Create similar user cluster and distinguish user cluster
based on IP address.
2. Create site clusters based on frequently accessed sites.
3. If number of sites in current site cluster is greater
than previous site cluster then assign that is the most
popular site.
4. Return the most popular site
5. Otherwise assume that is the least popular site
6. Return the least popular site and repeat until all user &
site clusters are processed.
VOB algorithm
Input
Web log files
Output
User cluster, site cluster, most popular site, least popular
site.
Algorithm
If (IP address is unique) then
Create similar_user_ cluster;
Return similar_ user_ cluster;
If (IP address is same and user name is not unique, agent log,
operating system and browsers are different ) then
Create distinguish_ user_ cluster.
Return distinguish_ user _cluster.
For i =sitecluster_1 to sitecluster_n do
If (no. of. sites in current site cluster > previous site
cluster) then
Most Popular = current_ site_ cluster
return “most popular site”
else
Least Popular = current_ site_ cluster
return “least popular site”
repeat until all user & site clusters are processed.
In the proposed method VOB, clustering plays a key role to
classify web visitors on the basis of user click history and
similarity measure. This algorithm considers four entities
namely IP address, user name, website name, and frequency
of accessed sites. Cookies based weblogs are taken as the
input which mainly classify the unique users and helps to
create user clusters.
Here, the website and webpage navigation behavior are
considered as the basic source for tracing the visitors’ online
behavior and also to identify the interest of the user in
accessing the various web sites. Based on the number of sites
in the site clusters, it is concluded that it is the most popular
website or the least one. Also the frequency is calculated by
taking the time difference and the total number of clicks on a
particular website given in a log file. Hence the VOB
algorithm effectively traces the behavior of online users which
supports the website usage analysis.
7. EXPERIMENTAL SETUP AND
PERFORMANCE ANALYSIS
The weblog files are collected from college web server and
browser machine for the period of 6 months from January
2013 to June 2013. For implementation, Java (jdk 1.6) is used
in the system which posses Intel core i3 processor with 4GB
RAM.
7.1 Performance evaluation
The performance evaluation is done by analyzing the dataset
taken. In the period of 6 months, the total no. of users is 5080.
By the proposed algorithm, web visitors are classified on the
basis of user click history and similarity measure. The
processed dataset is given below.
Table 3. Processed Dataset
Label Processed Value
Total No. of users 5080
Similar user cluster 2279
Distinguish user cluster 2801
The following “Fig 6.” shows the creation of user cluster.
Figure 6. User cluster creation
The VOB algorithm identifies the users based on the data
collected from cookies. This algorithm takes all the users in
count and their request for processing. By the result, the
proposed VOB algorithm outperforms to classify the similar
user cluster and distinguish user cluster.
The total number of sites visited by the user is calculated as
12682. Among these sites, maximum number of visits has
done for the educational websites. Totally it is of count 4700.
And the users have given next preference to the social
networking sites.
The number of visits made to social networking sites is 3269.
Also 3031 users have referred the research sites. Only from
the month of APRIL and MAY, the 1230 users have used the
electronic commerce websites.
The number of visits for the case of entertainment is 452
which explicitly shows the minimum desirability of that kind
of sites. The given “Fig 7.” tells that site clusters are created
based on frequently accessed sites.
From the following “Fig 8” it is known that the maximum
weighttage has given to educational sites than other sites like
entertainment, social, electronic commerce and research.
The most popular website is identified based on the condition
that if no. of. sites in current site cluster is greater than
previous site. Otherwise it was assumed that is the least
popular site This same procedure is repeated until all user and
site clusters have processed.
“Fig 9.” shows that, the proposed algorithm proves it’s
efficiency for classifying the preference of users to various
categories of websites.
0
1000
2000
3000
4000
5000
6000
User Cluster Creation
Distinguish
User Cluster
Similar User
Cluster
Total No.of
users
6. International Journal of Computer Applications (0975 – 8887)
Volume 87 – No.3, February 2014
27
Figure 7. Site cluster creation
Figure 8. Overall Performance of VOB algorithm
In this algorithm, user cluster and site cluster creation is
mainly considered as an important work and it helps to do
website usage analysis based on their website surfing
behavior.
8. CONCLUSION AND SUMMARY
Web usage mining has emerged as the essential tool for
realizing more personalized user-friendly and business
optimal web services. The key is to use the user-click stream
data for many mining purposes. Traditionally, web usage
mining is used by e-commerce sites to organize their sites and
to increase profits. The newly proposed algorithm is Visitors’
Online Behavior (VOB) which identifies user behavior and
creates user cluster, site cluster, most popular web site and the
least popular web site. It must to trace the visitors’ on-line
behaviors for website usage analysis. Actually it is an analysis
to get knowledge about how visitors use website which could
provide guidelines to web site reorganization and helps to
prevent disorientation.
Figure 9. Identification of Most Popular and Least
Popular Site
9. FUTURE ENHANCEMENT
A number of further tasks could be added by demonstrating
the utility of web mining. It can be done by making
exploratory changes to web sites. The intelligent system web
usage preprocessor splits the human and search engine
accesses before using the preprocessing techniques. This can
be extended by using some other learning algorithms also[1].
It can be further extended to user profiling and similar image
retrieval by tracing the visitors’ on line behaviors for effective
web usage mining[11]. Many preprocessing techniques can be
effectively applied in web log mining[7]. The preprocessing
of web log data for finding frequent patterns using weighted
association rule mining technique can be extended to other
industrial and social organizations too[6]. In recent days, the
web usage mining has great potential and frequently
employed for the tasks like web personalization, web pages
prefetching and website reorganization, etc[16]. So it is
required to know the users’ behavior when interaction is made
with the web.
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