Trends in Web Content and Structure Mining

CASPUR allows many academic Italian institutions located in the Centre-South of Italy to access more than 7 million articles through a digital library platform. The behaviour of its users were analyzed by considering their “traces”, which are stored in the web server log file. Using several web mining and data mining techniques the author discovered a gradual and dynamic change in the way articles are accessed. In particular there is evidence of a journal browsing increase in comparison to the searching mode. Such phenomenon were interpreted using the idea that browsing better meets the needs of users when they want to keep abreast about the latest advances in their scientific field, in comparison to a more generic searching inside the digital library.

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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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Extraction of Knowledge from Web Server Logs Using Web Usage Mining

Asian Journal of Computer Science and Technology ◽

10.51983/ajcst-2019.8.s3.2113 ◽

2019 ◽

Vol 8 (S3) ◽

pp. 12-15

Author(s):

B. Harika ◽

T. Sudha

Keyword(s):

Data Mining ◽

Web Mining ◽

Web Server ◽

Primary Source ◽

Web Usage Mining ◽

Data Mining Techniques ◽

Web Usage ◽

Browsing Behavior ◽

The Web ◽

Usage Data

Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.

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User's Behaviour inside a Digital Library

International Journal of Decision Support System Technology ◽

10.4018/jdsst.2010040106 ◽

2010 ◽

Vol 2 (2) ◽

pp. 52-59

Author(s):

Marco Scarnò

Keyword(s):

Data Mining ◽

Digital Library ◽

Web Mining ◽

Dynamic Change ◽

Web Server ◽

Scientific Field ◽

Data Mining Techniques ◽

Log File ◽

The Web ◽

The Way

CASPUR allows many academic Italian institutions located in the Centre-South of Italy to access more than 7 million articles through a digital library platform. The behaviour of its users were analyzed by considering their “traces”, which are stored in the web server log file. Using several web mining and data mining techniques the author discovered a gradual and dynamic change in the way articles are accessed. In particular there is evidence of a journal browsing increase in comparison to the searching mode. Such phenomenon were interpreted using the idea that browsing better meets the needs of users when they want to keep abreast about the latest advances in their scientific field, in comparison to a more generic searching inside the digital library.

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A Heuristic Mining Algorithm Using Web Hyperlink Structure

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.108-111.11 ◽

2010 ◽

Vol 108-111 ◽

pp. 11-16

Author(s):

Chun Lai Chai

Keyword(s):

Data Mining ◽

Web Mining ◽

Web Usage Mining ◽

Web Content ◽

Web Content Mining ◽

Web Structure ◽

Mining Algorithm ◽

Web Structure Mining ◽

Content Mining ◽

The Web

Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.

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Web Mining for the Integration of Data Mining with Business Intelligence in Web-Based Decision Support Systems

Advances in Business Strategy and Competitive Advantage - Integration of Data Mining in Business Intelligence Systems ◽

10.4018/978-1-4666-6477-7.ch007 ◽

2015 ◽

pp. 120-142 ◽

Cited By ~ 2

Author(s):

Marcos Aurélio Domingues ◽

Alípio Mário Jorge ◽

Carlos Soares ◽

Solange Oliveira Rezende

Keyword(s):

Data Mining ◽

Decision Support ◽

Decision Support Systems ◽

Business Intelligence ◽

Web Mining ◽

Support Systems ◽

Web Documents ◽

Web Based ◽

Use Of Data ◽

Computer Based

Web mining can be defined as the use of data mining techniques to automatically discover and extract information from web documents and services. A decision support system is a computer-based information system that supports business or organizational decision-making activities. Data mining and business intelligence techniques can be integrated in order to develop more advanced decision support systems. In this chapter, the authors propose to use web mining as a process to develop advanced decision support systems in order to support the management activities of a website. They describe the web mining process as a sequence of steps for the development of advanced decision support systems. By following such a sequence, the authors can develop advanced decision support systems, which integrate data mining with business intelligence, for websites.

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User’s Behaviour inside a Digital Library

Integrated and Strategic Advancements in Decision Making Support Systems ◽

10.4018/978-1-4666-1746-9.ch011 ◽

2012 ◽

pp. 138-146

Author(s):

Marco Scarnò

Keyword(s):

Data Mining ◽

Digital Library ◽

Web Mining ◽

Dynamic Change ◽

Web Server ◽

Scientific Field ◽

Data Mining Techniques ◽

Log File ◽

The Web ◽

The Way

CASPUR allows many academic Italian institutions located in the Centre-South of Italy to access more than 7 million articles through a digital library platform. The behaviour of its users were analyzed by considering their “traces”, which are stored in the web server log file. Using several web mining and data mining techniques the author discovered a gradual and dynamic change in the way articles are accessed. In particular there is evidence of a journal browsing increase in comparison to the searching mode. Such phenomenon were interpreted using the idea that browsing better meets the needs of users when they want to keep abreast about the latest advances in their scientific field, in comparison to a more generic searching inside the digital library.

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Ontology Based Data Mining Approach on Web Documents

Computer and Information Science ◽

10.5539/cis.v7n4p123 ◽

2014 ◽

Vol 7 (4) ◽

pp. 123

Author(s):

Hamideh Hajiabadi

Keyword(s):

Data Mining ◽

Data Sources ◽

Text Documents ◽

Web Documents ◽

Valuable Data ◽

Data Mining Techniques ◽

Data Mining Approach ◽

Huge Data ◽

Data Source ◽

Extract Information

Internet which is included plenty of huge data source is now rapidly increasing in all domains. It is considered as valuable data sources if the data can be processed that results in information. Data mining techniques are widely utilized in web documents in order to extract information. In this paper a data mining approach based on Ontology is proposed to classify web documents in order to facilitate applications based on classified text documents like search engines. The proposed approach is implemented and applied on several web documents. The experimental results show considerable progress.

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A structured model for evaluating countermeasures at highwayrailway grade crossings

Canadian Journal of Civil Engineering ◽

10.1139/l05-017 ◽

2005 ◽

Vol 32 (4) ◽

pp. 627-635 ◽

Cited By ~ 3

Author(s):

Young-Jin Park ◽

Frank F Saccomanno

Keyword(s):

Data Mining ◽

Statistical Models ◽

Decision Support Tool ◽

Cost Effective ◽

Expected Number ◽

Structured Model ◽

Support Tool ◽

Data Mining Techniques ◽

Use Of Data ◽

Grade Crossings

Various countermeasures can be introduced to reduce collisions at highwayrailway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highwayrailway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highwayrailway grade crossing, collision prediction model, countermeasures, Poisson regression.

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