A Java Technology Based Distributed Software Architecture for Web Usage Mining

In this chapter we focus on the three approaches that seem to be the most successful ones in the Web usage mining area: clustering, association rules and sequential patterns. We will discuss some techniques from each one of these approaches, and then we will show the benefits of using METALA (a META-Learning Architecture) as an integrating tool not only for the discussed Web usage mining techniques, but also for inductive learning algorithms. As we will show, this architecture can also be used to generate new theories and models that can be useful to provide new generic applications for several supervised and non-supervised learning paradigms. As a particular example of a Web usage mining application, we will report our work for a medium-sized commercial company, and we will discuss some interesting properties and conclusions that we have obtained from our reporting.

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Traversal Pattern Mining in Web Usage Data

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch119 ◽

2008 ◽

pp. 2004-2021

Author(s):

Jenq-Foung Yao ◽

Yongqiao Xiao

Keyword(s):

Pattern Mining ◽

Pattern Discovery ◽

Web Usage Mining ◽

Sequential Patterns ◽

Web Usage ◽

Web Logs ◽

Frequent Episodes ◽

Browsing Behavior ◽

The Web ◽

Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

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Traversal Pattern Mining in Web Usage Data

Web Information Systems ◽

10.4018/978-1-59140-208-4.ch010 ◽

2004 ◽

pp. 335-358 ◽

Cited By ~ 2

Author(s):

Yongqiao Xiao ◽

Jenq-Foung (J.F.) Yao

Keyword(s):

Pattern Mining ◽

Pattern Discovery ◽

Web Usage Mining ◽

Sequential Patterns ◽

Web Usage ◽

Web Logs ◽

Frequent Episodes ◽

Browsing Behavior ◽

The Web ◽

Usage Data

Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are also discussed.

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Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2018.7.3.1902 ◽

2018 ◽

Vol 7 (3) ◽

pp. 39-43

Author(s):

Satyaveer Singh ◽

Mahendra Singh Aswal

Keyword(s):

Web Usage Mining ◽

Web Usage ◽

Navigation Patterns ◽

Product Recommendations ◽

Taxonomical Classification ◽

The Comparative Study ◽

The Web ◽

Real World Problems

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

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Web Usage Mining and the Challenge of Big Data

Big Data ◽

10.4018/978-1-4666-9840-6.ch042 ◽

2016 ◽

pp. 899-928

Author(s):

Abubakr Gafar Abdalla ◽

Tarig Mohamed Ahmed ◽

Mohamed Elhassan Seliaman

Keyword(s):

Data Mining ◽

Pattern Discovery ◽

Web Usage Mining ◽

Data Mining Techniques ◽

Web Log ◽

Web Usage ◽

Web Logs ◽

Usage Patterns ◽

Rich Data ◽

The Web

The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

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Semantic Web Adaptation

Web Technologies ◽

10.4018/978-1-60566-982-3.ch006 ◽

2011 ◽

pp. 78-88

Author(s):

Alexander Mikroyannidis ◽

Babis Theodoulidis

Keyword(s):

Semantic Web ◽

Web Sites ◽

Web Usage Mining ◽

New Era ◽

Automatic Reasoning ◽

Web Usage ◽

User Friendly ◽

Unstructured Information ◽

The Web ◽

The Way

The rate of growth in the amount of information available in the World Wide Web has not been followed by similar advances in the way this information is organized and exploited. Web adaptation seeks to address this issue by transforming the topology of a Web site to help users in their browsing tasks. In this sense, Web usage mining techniques have been employed for years to study how the Web is used in order to make Web sites more user-friendly. The Semantic Web is an ambitious initiative aiming to transform the Web to a well-organized source of information. In particular, apart from the unstructured information of today’s Web, the Semantic Web will contain machine-processable metadata organized in ontologies. This will enhance the way we search the Web and can even allow for automatic reasoning on Web data with the use of software agents. Semantic Web adaptation brings traditional Web adaptation techniques into the new era of the Semantic Web. The idea is to enable the Semantic Web to be constantly aligned to the users’ preferences. In order to achieve this, Web usage mining and text mining methodologies are employed for the semi-automatic construction and evolution of Web ontologies. This usage-driven evolution of Web ontologies, in parallel with Web topologies evolution, can bring the Semantic Web closer to the users’ expectations.

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Web Usage Mining Issues in Big Data

Impacts and Challenges of Cloud Business Intelligence - Advances in Systems Analysis, Software Engineering, and High Performance Computing ◽

10.4018/978-1-7998-5040-3.ch007 ◽

2021 ◽

pp. 102-112

Author(s):

Sunny Sharma ◽

Manisha Malhotra

Keyword(s):

Data Mining ◽

Big Data ◽

User Behavior ◽

Web Usage Mining ◽

Web Personalization ◽

Data Mining Techniques ◽

Meaningful Information ◽

Web Usage ◽

Use Of Data ◽

The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

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Effectiveness of Web Usage Mining Techniques in Business Application

Advances in Data Mining and Database Management - Web Usage Mining Techniques and Applications Across Industries ◽

10.4018/978-1-5225-0613-3.ch013 ◽

2017 ◽

pp. 324-350 ◽

Cited By ~ 2

Author(s):

Ahmed El Azab ◽

Mahmood A. Mahmood ◽

Abd El-Aziz

Keyword(s):

Data Mining ◽

Academic Research ◽

Web Usage Mining ◽

Web Pages ◽

Web Data ◽

Web Data Mining ◽

Web Usage ◽

Business Application ◽

Common Interests ◽

The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

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Methodologies and Techniques of Web Usage Mining

Advances in Data Mining and Database Management - Web Usage Mining Techniques and Applications Across Industries ◽

10.4018/978-1-5225-0613-3.ch011 ◽

2017 ◽

pp. 275-296

Author(s):

T. Venkat Narayana Rao ◽

D. Hiranmayi

Keyword(s):

Web Mining ◽

Pattern Analysis ◽

Pattern Discovery ◽

Secondary Data ◽

Web Usage Mining ◽

Sequential Patterns ◽

Useful Knowledge ◽

Web Usage ◽

Automatic Discovery ◽

Collection Data

Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. It is the type of Web mining activity that involves the automatic discovery of out what users are looking for on the Internet. In this chapter methodology of web usage mining explained in detail which are data collection, data preprocessing, knowledge discovery and pattern analysis. The different Web Usage Mining techniques are described, which are used for knowledge and pattern discovery. These are statistical analysis, sequential patterns, classification, association rule mining, clustering, dependency modeling. Pattern analysis is needed to filter out uninterested rules or patterns from the set found in the pattern discovery phase.

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Web Usage Mining through Associative Models

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch232 ◽

2011 ◽

pp. 1231-1234 ◽

Cited By ~ 3

Author(s):

Paolo Giudici ◽

Paola Cerchiello

Keyword(s):

Web Site ◽

Web Usage Mining ◽

Web Pages ◽

Web Page ◽

User Session ◽

Web Usage ◽

Associative Models ◽

The Web

The aim of this contribution is to show how the information, concerning the order in which the pages of a Web site are visited, can be profitably used to predict the visit behaviour at the site. Usually every click corresponds to the visualization of a Web page. Thus, a Web clickstream defines the sequence of the Web pages requested by a user. Such a sequence identifies a user session.

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